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Chess Opening Theory/1. e4/1...e5/2. Bb5/2...Bc5. = Portuguese Opening = This is a good developing move. 3. b4!? is the dubious Miguel Gambit.

Chess Opening Theory/1. e4/1...e5/2. Bb5/2...Bc5/3. b4. = Portuguese Opening, Miguel Gambit = This is a dubious Gambit. Black may try to accept it.

Chess Opening Theory/1. d4/1...Nf6/2. c4/2...e6/3. Nc3/3...Bb4/4. a3/4...Ba5. =Sämisch Variation= 4...Ba5?? Black failed to notice the fate of retreating the bishop to a5. White has a deadly move /5. b4/.

Chess Opening Theory/1. d4/1...Nf6/2. c4/2...e6/3. Nc3/3...Bb4/4. a3/4...Ba5/5. b4. =Sämisch Variation= 5. b4. White punished black's careless play and will win black's bishop. Black may hold on to it with /5...Bb6/, but White can still play 6. c5.

Chess Opening Theory/1. d4/1...Nf6/2. c4/2...e6/3. Nc3/3...Bb4/4. a3/4...Ba5/5. b4/5...Bb6. =Sämisch Variation= 5...Bb6. White may now trap the bishop with /6. c5/.

Chess Opening Theory/1. d4/1...Nf6/2. c4/2...e6/3. Nc3/3...Bb4/4. a3/4...Ba5/5. b4/5...Bb6/6. c5. =Sämisch Variation= 6. c5. White has successfully won the bishop and will capture it next move.

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Bb4. =Open Game= A very dubious Gambit. This has never been played in serious games, and is just a way to look silly.

Kitchen Remodel/Cabinet front styles. Cabinet front styles. My choices. Because my kitchen includes three distinctly set apart spaces, I chose differently looking front elements: The gray I chose because it would go well with the reddish brown tones (shelves, flooring) that I had in mind combining it with and because it would bring the white of the countertop out. I was planning on two waterfalls which were supposed to get "all" the attention. Another reason for this selection was that those gray fronts came with integrated handles (routed grooves), which I liked a lot, not just for the looks, but also because the absence of protruding handles allowed us an even more efficient use of the available space (smaller fillers!). The product name is "Voxtorp". The white, also "Voxtorp", was a no-brainer because the pantry is a very narrow space, and the snow white fronts would make it feel both clean and a little less cramped. For the dining space, we had originally planned to splurge and to purchase an Ikea-hack from SemiHandmade. They offered a product that we found extremely cool: custom arrays of cabinet fronts that share one large sheet of walnut veneer. So the elements look as if the were cut out of one huge piece of wood. Unfortunately, we had run out of money and chose the next best thing that we could still afford, which was Ikea's "Sinarp" (oak veneer). It turned out to be an excellent choice because it very much matches the shade of our dining room table and chairs. Little theory of cabinet fronts. Materials. The most common material for cabinet fronts are high density fiber board and "solid hardwood", either coated with thermofoil or melamine, or painted or veneered. Typical specific woods are maple, cherry, oak, red oak, alder, hickory, walnut, birch, poplar, and mahogany. Types. The traditional type of a cabinet front element in the United States is the panel door (see image: C), made out of four solid wood frame pieces which enclose a (thinner) panel. Slab doors are made out of one solid piece of wood or engineered wood (A) and commonly associated with a more modern appearance. Some doors, for example plank style doors, fit into neither of those categories. <br clear=all> Panel type. The most basic panel door is the Shaker door, which is defined by a cope+stick construction… …and a complete absence of decorative elements; the panel is flat and recessed and there is absolutely no moulding, neither on the panel nor on the frame. Due to their minimalism, Shaker doors pass for all-rounders that suit both a modern and a traditional kitchen. This is certainly a matter of taste and therefore not disputable, but as the famous German filmmaker Alexander Kluge once said: "In Danger and Deep Distress, the Middleway Spells Certain Death". However, in my opinion, the cool thing about panel doors is that virtually every seemingly inconsequential part of those can be subject to adornment or variation: Consequently, there is an immense range of different designs in panel doors, even within the assortments of mainstream manufacturers: Some manufacturers also differentiate between "one pass", "two pass", "three pass" etc. Those numbers refer to the number of passes that a router needs to take to produce a specific moulding profile. Although I have a strong personal preference for the modern style and therefore naturally gravitate towards slab doors, I am in awe of cabinet artists. They know exactly where to position an ogee moulding and where not or when it should be accompanied by a cove or a bead or just a little step or a chamfer. They also know what is historically accurate and what is not. This is an amazing set of skills. Slab type. Slab doors come with laminate, thermofoil, melamine, lacque, high-gloss, veneer, paint-grade, stain-grade or metal surfaces among others. The surface can be smooth, textured or grooved; it can even show moulding. Some slab doors imitate the appearance of a panel door. But apart from such attempts to mock a traditional door, hinted frames on slab doors tend to be much narrower than on panel doors. Overlays. Another important parameter in cabinet front elements is overlay. Overlay is the width and height of a drawer front or cabinet door in relation to aperture's and the cabinet frame's dimensions: Handles. Crucial for the overall appearance of a cabinet front is also the choice of handles. There are multiple types: <br clear="all"> Ikea-hacks. If money is not a problem, any IKEA kitchen can be completed with front elements from a different manufacturer. There is a considerable number of companies worldwide who are specialized in "Ikea-hacks": products that are made to modify or to complete Ikea products. In the U. S. for example, there are, among others:

Short guide to the use of laser cutting machines/Use of Inkscape for Laser Cutting. In this sections of the book will be described how to use Inkscape software to create patterns to be used in laser cutting machines. Introduction. Inkscape is a free and open-source software for to drawing, design and edition of vector graphics, this make it ideal to create images or design and use them to cut materials using laser cutting machines. Current Inkscape version is 1.3.2, it can be downloaded from the Inscape webside, it also includes a free portable version (1.2.1) that can be downloaded from its website. Create a new file. Open Inkscape and start working in the project... the color options and unit measures can be changed at any time. Menues. Left menues. To create lines, draw images, create shapes or designs will be used Inkscape left menu, the most used options are: Pen, pencil, text, shapes tools, etc. with them will be created lines, draw other images, make shapes such as rectangles, circles, etc. Or create new designs. Left menues. The right menues are the following: Layers. It allow to choose all the layers in the image, each time a line or object is drawn (with color or not) a new layer is created. If all the layers are selected, can be grouped as one image. Export. In this menu can be choose the details of the image: size, format, etc. and export the project as a image, svg or any other format. Draw a line. Draw a line with the pen tool (the icon of a pen), press left click on the canvas to draw the first point, then press the left click in other part of the canvas to trace or make a line. In order to see the lines created choose a color and a stroke in the right menu. Even when a line is easy to make and does not look to useful, can be use to cut pieces in a straight line (or curved line) in the cases we just want to cut a piece of material to reduce its form or make a new form. Curved lines can be created choosing a initial dot and maintaining pressed the right click after pressing in the second point and moving it until the desired curve image is drawn. Creation of texts. Select the option text (icon A in the left menu), type the text and then modify its size and color. Change the stroke color to red if the text will be cut (rf00000) and the width of stroke to 0.72 points to cut. If the object will be edged or raster use blue or other color and stroke. Creation of shapes. Choose the shape tool, select a shape and draw it to create a rectangle, circle, star, etc. A tool that is used to properly adjust the dots that form part of the lines or objects is the rule. Creation of more forms. New forms can be easily created with the pen tool, just draw 2 or more points, then adjust their position of each dot changing the value for x (diagonal point) or y (vertical point). In inkscape this values are located in central and top area of inkscape and is always visible, to change them, just select the dot to be modified, beside them also can be changed the units from mm to inches or points. x value of 0 is located at the beginning of the page in the left size and it increases towards the right side. y value of 0 is located at the beginning of the page in the top of the page and it increases towards the bottom of the page (canvas). The coordinates are written in the form (x, y), for example the first dot can be located in 2 inches beside the left corner of the canvas and 2 inches down of the top of the canvas, making the first dot (2, 2). For example: To create a triangle, can be used 3 points, to create a rectangle use four points. The shape tool can also be used. A tool that is used to properly adjust the dots that form part of the lines or objects is the rule. Round corners. The corners of created objects (with pen tool or shape tool, such as the rectangles and squares tools) can be rounded choosing the option "select paths and noodles" in the right menu (below the option select options) and the option round corners in the top menu draw, also can be choose the dots and move the circled widget dot until the desired roundness is achieved. Use of the rule. To see the rule in the canvas press Ctrl + R or select view, rulers, show rulers. A measuring tape can be used to measuring of physical objects or planed designs, and the using these measures to draw them in the canvas using the rule to choose the dots. Use of the coordinates for each dot or object. The rule may not be necessary when using the x and y values, for example, drawing a straight line of 2 inches, the first dot can use the coordinate (2,2) and the second dot can use the coordinate (4, 2). If a object has more than one dot, they can be aligned in the same way, for example a rectangle with 4 dots, 2 inches long can have the following dots: (4, 4). The objects can also be aligned with the align tool, for this, click in the window menu and choose align (control+shift +A) after selecting the edit nodes option and then using the right menu. Changing the color of the lines or shapes. To change the color of a line or an object choose the option Fill: In this option the colors can be changed from CYMK to RGB, for cutting change the RGBA number to FF0000 (red). To change the color of a line or an object, click on the object, then in the properties window, appearance section, click on the square beside the fill option and in the filling space beside #, type the color number. RGB color model uses a system of 6 number and letters, the three more used colors in laser cutting to indicate what type of task will be done with the line or object are the following: Black, to raster etching (00000) Blue, to vector etching (0000FF) Red, to cut Cutting (ff00000) Choose the color palette icon and type ff0000, this will create a red color, this will indicate the printer that the line or object will be cut, or 000000 to raster etch, or 0000ff to vector etch. Cutting, vector etching and raster etching. Once you have designed an object use the color numbers to complete the desired task. Cutting. Use red color, ff00000, this will cut the object in the desired shape. Raster etching. Use black color, 00000, this will vector etch the design. Vector etching. Use blue color, 0000FF, this will vector etch the design. Create a path or vectorize a raster image. To vectorize an image with Inkscape select the following options: From the "path" menu choose the option "Trace bitmap", the the option "brightness cutoff", and select multiple scans (by colors 8 or 20). The option "object un-group", This will create 8 or 20 (depending the number of color selected), it will be 20 layers to choose between the parts, they will be overlay one over each one, they will be vectors. If the options Path, un-group are selected, each layer can be moved to seen separated and be used or deleted. For logos or simple design can be selected 2 colors to separate use 2 (or 4 or 8) for logos. Move all not needed, leave the image or images that represent the desired image to use. Then can close path, and select image size to change its size and save it as svg, this image can also be seen in illustrator or be used in laser cutting machines. Other useful options are outside edge e inside edge, these can be erased, and the option simplify to remove the number of dots. The different colors allow to control and choose the order in which the object will be cut. Print to sent the file to the laser cutting machine. To sent the document created to the laser cutting machine just press the keys Ctrl + p and choose the laser cutting machine, after this the file will be sent to the Universal Laser Systems control panel software, and from them the cutting can be started. See also. Short guide to the use of laser cutting machines/Use of Adobe Illustrator for Laser Cutting

Chess Opening Theory/1. d4/1...d6/2. c4/2...e5. =Rat Defence= The Rat Defence is a solid option for black against the Old Indian. It mainly challenges white’s center and fights for development. White may take the pawn with 3. dxe5.

Chess Opening Theory/1. d4/1...d6/2. c4/2...e5/3. dxe5. =Rat Defence= At this point, black has 2 main options. /3...dxe5/ is surprisingly popular, but black is slightly worse according to the engine. Better is /3...Nc6/, the Lisbon Gambit, developing aggressively and attacking the e5 pawn.

Chess Opening Theory/1. d4/1...d6/2. c4/2...e5/3. dxe5/3...Nc6. =Rat Defence= Nc6 is the key move to the Lisbon Gambit. White can greedily capture another pawn with /4. exd6/, but must play accurately.

Chess Opening Theory/1. f4/1...e5/2. fxe5/2...d6/3. exd6/3...Nf6. 3...Nf6. This is the Langheld Gambit, a dubious pawn sacrifice to create lots of unknown traps and trick the white players to fall for them.

Chess Opening Theory/1. e4/1...e6/2. Bc4. 2. Bc4 this move is playable but allows black to play /2...d5/ and challenge the white center

Chess Opening Theory/1. d4/1...g5. = 1... g5??: Borg Gambit = First Impressions. 1... g5, the Borg Gambit is a rather rare and unsound response to 1. d5. This move is generally considered inferior to 1... d5 or 1... Nf6 by most masters as Black is giving up a pawn, weakening the kingside defense, and promoting White's development of the queen's side bishop in exchange for developing the king's side bishop in a . Ideas/Responses for White. A controlling move that White can do is 2. e4, taking space in the center and opening up the king's bishop and queen, allowing for rather fast development. The question that White poses on Black is if to continue with the hypermodern idea with 2... Bg7 or try to fight back in the center with 2... d5. Another move White can do is just take the pawn with 2. Bxg5, allowing for development as well as capturing a pawn. Black may play 2. f6? to kick White's bishop away and gain a tempo, but this can be countered with 3. e4!, making the bishop poisoned by a checkmate with 4. Qh5# if Black greedily takes the bishop with 3...fxg5??. Ideas/Responses for Black. Center Attack. If White takes the g5 pawn, then there are two main ideas for Black to pursue. Black could go take space in the center with 2...f6 trying to gain tempo and eventually break into the center with 3.e4, also actually kicking the bishop out as the Qg5 checkmate isn't instantaneous now. However, this leads to a devastating attack against Black with Qg5 forcing the king into the open, where White can develop and attack the weak king with Nf6, Bc4, and dxe5. Fianchetto. Another idea is to fianchetto the bishop if White does not take and to play a pseudo-King's Indian Defense with d6, Nf6, and other hypermodern moves. Theory table. 1. d4 g5

Chess Opening Theory/1. e4/1...g5/2. g4. = Borg Defence: Symmetric Variation = ECO:B00. Symmetric Variation.

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bc4/3...d6/4. Nc3/4...Bg4. =Paris Defence=

Inclusive Data Research Skills for Arts and Humanities/How to create an account. To fully take part in this collaborative book, we recommend creating an account. This will help with attribution and make the writing process easier as you gain extra permissions over time. If you already have a Wikipedia account, the log in details will also work on Wikibooks At the bottom of this page is a video demonstrating how to create an account. You can also follow the step-by-step process written below: https://www.youtube.com/watch?v=-zk-p6940i0

Inclusive Data Research Skills for Arts and Humanities/How to add a chapter. Each page linked to this Wikibook is called a chapter. Here you will learn how to add a new chapter to the book, starting with adding it to the list of content and then adding text. If you title you want to write is already present, you can skip step. The information is also available as a video. https://www.youtube.com/watch?v=YOmBhiMn6g0

Inclusive Data Research Skills for Arts and Humanities/What is Wikibooks. Below is a short video explaining what Wikibooks is and how our Wikibook is organised. https://www.youtube.com/watch?v=Qk9yHAMBuK4

Chess Opening Theory/1. e4/1...e5/2. f4/2...exf4/3. Nf3/3...g5/4. Bc4/4...g4/5. O-O/5...gxf3. =King's Gambit, Muzio Gambit= 5...gxf3. Black has accepted the Muzio Gambit and must play accurately. White may want to capture the pawn with /6. Qxf3/.

Chess Opening Theory/1. e4/1...e5/2. f4/2...exf4/3. Nf3/3...g5/4. Bc4/4...g4/5. Ne5. White wills to save the knight while allowing 5...Qh4+ attacking the king.

Chess Opening Theory/1. e4/1...e5/2. f4/2...exf4/3. Nf3/3...g5/4. Bc4/4...g4/5. Ne5/5...Qh4. /6. Kf1/: Main Line

Chess Opening Theory/1. e4/1...e5/2. f4/2...exf4/3. Nf3/3...g5/4. Bc4/4...g4/5. Ne5/5...Qh4/6. Kf1. /6...Nh6/: Silberchmidt Defence /6...Nc6/: Viennese Variation /6...f3/: Cochrane Gambit /6...Nf6/: Santa Maria Defence

Chess Opening Theory/1. e4/1...d6/2. Qg4. =Pirc Defense= 2. Qg4?? This mess allows black to get a winning position. Here is what happens next: 1. e4 d6 2. Qg4??

Chess Opening Theory/1. e4/1...Nc6/2. Bb5. =Nimzowitsch Defence: Pseudo-Spanish Variation= References. 1. e4 Nc6 2. Bb5

Chess Opening Theory/1. e4/1...Nc6/2. Bb5/2...Nd4. =Nimzowitsch Defence: Pseudo-Spanish Variation= References. 1. e4 Nc6 2. Bb5 Nd4

Chess Opening Theory/1. e4/1...Nc6/2. Bb5/2...Nd4/3. Bc4. =Nimzowitsch Defence: Pseudo-Spanish Variation= References. 1. e4 Nc6 2. Bb5 Nd4 3. Bc4

Antenna Television/Market Listings/Arizona. Tucson-Sierra Vista. Counties. Arizona Sonora (Mexico) Yuma-El Centro. Counties. Arizona California Baja California (Mexico) Sonoroa (Mexico)

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bb5/3...Nge7. =Ruy Lopez, Cozio Defence= Cozio Defence. Black defends the knight with the other knight.

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bb5/3...f5/4. exf5. = Schliemann Defence Accepted = This move is alright, but gives Black a slight edge, as they will regain the lost pawn with extra tempi and space after 4... e4. White's main move is then 5. Qe2, pinning and attacking the e4 pawn. 5... Qe7 is best for Black, avoiding the pin and renewing the threat on the knight on f3. Now White can try 6. Bxc6 with the idea of moving the knight to d4 to protect f5. If 6... dxc6, 7. Nd4 can be followed by Ne6 in some lines, such as after 7... Qe5. After 6... bxc6 7.Nd4, Black will likely play 7... Nf6, and eventually either Qe5 or c5 and then d5 to try to win the pawn back. If 5. Bxc6, Black will play 5... dxc6 and Bxf5 with a White can also try 5. Ng1, threatening Qh5+, and Black will likely respond 5... Qg5.

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bb5/3...f5/4. exf5/4...e4. = Schliemann Defence Accepted = /5. Ng1/ is an interesting move but best to keep a complex position

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bb5/3...f5/4. exf5/4...e4/5. Ng1. = Schliemann Defence Accepted = Moves: /5...Nf6/ /5...Qg5/

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bb5/3...f5/4. exf5/4...e4/5. Ng1/5...Qg5. = Schliemann Defence Accepted = Moves: /6. g4/

Sylheti/Slogans. The Melody of Unity in the Sylhet Referendum. During the Sylhet referendum, a poignant song echoed the sentiments of those advocating for the unity of Sylhet. Sung in the local language (Sylheti), the song rhetorically questioned whether one would choose to live under a tree after destroying their hut, symbolizing the potential division of Sylhet. The heartfelt plea urged against breaking Sylhet into pieces. Unfortunately, despite these appeals, the Sylhet region was divided between India and Pakistan in 1947. The song captures the emotional plea to preserve the unity of Sylhet during a significant historical moment. Transcription. Transcription by Chatterjee:

Chess Opening Theory/1. d4/1...d5/2. e4/2...dxe4/3. Bc4. =Blackmar-Diemer Gambit (BDG)= 3. Bc4. This move develops the bishop to its most active square. The most common response is Nc6.

Chess Opening Theory/1. d4/1...d5/2. e4/2...dxe4/3. Bc4/3...Nc6. =Blackmar-Diemer Gambit (BDG)= 3...Nc6. This is the most common and best response. White will mainly continue with Qh5.

Chess Opening Theory/1. d4/1...d5/2. e4/2...dxe4/3. Bc4/3...Nc6/4. Qh5. =Blackmar-Diemer Gambit (BDG)= 4. Qh5. White threatens to win a queen by checking on f7. Black should play g6 to stop the threat. However, if he, in his hotheaded desire plays Nf6??, Black faces doom with Qxf7+ Kd7 Be6+ Kd6 Bf4+ Ne5 Bxe5+ Kc6 Nc3 Bxe6 Qxe6+ Qd6 Bxd6.

Chess Opening Theory/1. e4/1...e5/2. Bc4/2...Bc5/3. Qh5/3...Qe7. =Bishop's Opening: Classical Defence= 3...Qe7. This is the move black needs to defend f7, the alternative, 3...Qf6, faces a small amount of problems after 4. Nc3 5. Nd5.

Chess Opening Theory/1. d4/1...e5/2. dxe5. =Englund Gambit Accepted= References. /1. d4/1...e5/2. dxe5/2...d6 /1. d4/1...e5/2. dxe5/2...Nc6

Chess Opening Theory/1. e4/1...e5/2. Nf3/2...Nc6/3. Bc4/3...Nf6/4. O-O. =Italian Game, Two Knights Defence= 4. O-O. White sacrifices a pawn for a lead in development and king safety. This is not the soundest line in the Italian Game, but completely playable. 1. e4 e5 2. Nf3 Nc6 3. Bc4 Nf6 4. O-O

Kitchen Remodel/Template/Construction. <noinclude>

Manshu/Chapter 1. Translator's note. In general for many portions I am confident of a reasonable translation. However, there are also more than a few areas where I am certain there are errors: to what degree, however, I am less sure. In general you should be able to interpret the confidence of translation by the depth of notes and/or mapping around the passage(s) in question. The final portion is particularly murky. The sections are of my own construction. Differences with previous Gordon H. Luce Translation. I am currently (December 2015) reviewing this portion of the translation against Gordon H. Luce's Previous English Translation. This should allow the detection of errors and/or points of disagreement for further investigation and comment. The following list excludes systemic and stylistic differences and focuses on objective issues. Distances within Yunnan and at its Borders (云南界内途程; "yúnnán jiè nèi túchéng"). Chéngdū (成都府) to Yángxiefai (陽苴咩城). Note that the borderland region between modern Yunnan proper and the Sichuan basin through which this section's route passes is historically inhabited by the Yi. The Yi were both powerful and independent until after the Yuan Dynasty when their princess made a deal with the invading Mongols. They wore impressive armour, examples of which can be seen online here at Sichuan University Museum and here at Harvard, or in person at the Anthropology Museum of Yunnan University on Beimenjie in Kunming. Politics. Note that this entire portion is taken to have been a latter explanatory diversion, and not part of the main body of text.

Manshu/Chapter 2. Translator's note. In general I am confident of a reasonable translation. The sections are of my own construction. Maps are yet to draw, and cross-referencing with existing identifications of some foreign names will be required.

Manshu/Chapter 3. Translator's note. This section is relatively difficult to translate given the frequency of weird nomenclature, some nontrivial grammar structures with inadequate context, and the degree to which the geographical context jumps about. I am doing my best but you should regard the below as mostly questionable.

Manshu/Chapter 4. Translator's note. This section appears to be quite fun and a lot easier than the last chapter thank deity-of-choice for that! The sections and their titles are of my own creation. Translation. The "Máng​ Barbarians" (茫蠻; ie. Pyu?) south of "Yǒng​chāng​" (永昌城; ie. modern Baoshan). This section may refer to the Pyu city-states of upper Burma. This section almost certainly requires additional splitting; however further interpretation on internal context changes must be done to achieve this.

Manshu/Chapter 5. Translator's note. The title and introduction of this chapter uses a very rare character. I presume the meaning is "markets". The character in question, which can be viewed at the internet archive's scanned copy of the "Complete Library of the Four Treasuries"《四库全书》edition (courtesy of Zhejiang University Library and archive.org) and which also has an entry at Chinese Wiktionary (with no known meanings listed), combines 贝 (meaning shell, and later shell money — as supposed in the Sino-Tibetan etymology notably including a similar phonetic in the Kachin language, and a not dissimilar Proto-Zhuang-Tai record) with 佥 (meaning a gathering), and is thus in a rough semantic sense plausibly a term used for local markets in the region where the use of shells as currency was widespread and well documented — to Westerners, famously allegedly by Marco Polo — through at least the Yuan dynasty.The character is actually 𧸘 in Unicode however that usually only displays as an empty box (sometimes known as "tofu") on most computers owing to font limitations on rare Chinese characters. Mandarin pronunciation (as irrelevant as that may be for a source of this age) is alleged by Chinese Wiktionary (probably direct from the Unicode Unihan database) as being "liàn", "biǎn", or "jiǎn" — for what that's worth... the earlier two having some plausible phonetic similarity with the Proto-Sino-Tibetan and Proto-Zhuang-Tai, respectively. My probably reliable general comprehension is that the closing consonant is the least reliable portion of a character's phoneme when brought forward to Mandarin from ancient pronunciation, with many closing sounds (evidenced for example with final 'k' in Cantonese, which is more honest with respect to Tang Dynasty pronunciations) dropped entirely or morphed in to softer variants. Thus instead of ("lian" or "bian" or "jian") we can vaguely reconstruct the phoneme as follows — ("l" or "b" or "j") + (possibly an "ee" type sound, or some longer form or dipthong variant thereof) + (optionally some kind of closing consonant: probably not "n"). We can probably get some further input from an appropriately experienced linguist here. Translation. Introduction. This is apparently not part of the original text, because it consists of one short clarifying note and the rest is entirely latter-day comments; ie. those referencing later texts and those known from markup style to be added by later transliterators.

Manshu/Chapter 7. Translator's note. Translation is complete but it has not yet been rechecked.

Manshu/Chapter 8. Translator's note. There is a substantial amount of linguistic material here which would benefit from analysis by appropriately skilled academics or native speakers.

Manshu/Chapter 9. Translator's note. This section has been started and seems at first to be very connected in terms of content to the end of Chapter 8.

Manshu/Chapter 10. Translator's note. The translation of this section has begun but is incomplete. The sections are of my own creation.

Manshu/Outstanding Work. For the convenience of others here are listed any potentially useful tangents for further research that came to mind during my interpretation of the text.

Manshu/Influences. Cartographic. The fairly detailed geographic knowledge of the region detailed in the text does not seem to be widely adopted the dynasties following the Tang. One example is the much-lauded map of China in the （碑林） in Xi'an, sometimes considered a marvel of Song Dynasty cartography, which shows a very confused and very blank view of the region. Only the Stone Gate (石门) and Qujing (曲靖) are shown in relatively correct locations. Lake Dian (滇池) and an unidentified place known as Black River Mouth (黑水口) are shown bordering a south-easterly flowing river, directly to the west of Annan (安南府; ie. Hanoi) which, while probably referring to the Red River (红河), veers southward more like the Mekong River (澜沧江) and spuriously misses its mark, where it should have flowed through Annan (安南府) and onward to the South China Sea. This may suggest that the "Manshu" (蠻書) was not widely reproduced or distributed, pointing to it being more of a Tang period political text prepared prior to the Tang armies' invasions rather than a broadly distributed dossier. The Yunnanese region was probably of limited interest to the majority of literate Chinese of these periods, being remote, relatively dangerous and possibly of limited trade value and historical/political significance.

Manshu/Translator's Note. Other sources. Other sources for the Nanzhao period that could inform the translation include the following. About the translation. Hi there. I'm not a 'professional translator' but have lived in the Yunnan area on and off for 15 years and have some (very) limited academic background in ancient Chinese history and maintain it as an interest. Regarding this text, there are allegedly pre-existing translations in English, French and Vietnamese. I have also learned of a recent annotated publication of a different but related text — Fan Chuo, "Yunnanzhi Buzhu" ("Supplementary annotation to the Record of Yunnan"), annotated by "Mu Qin" (昆明 雲南人民出版社). None of those works were viewed or consulted before or during the preparation of this translation, with the exception of the introduction to Gordon H. Luce's 1961 publication for which I received an online source after translating up to chapter 4 already. Basically it confirms my own impressions, which is a good sign, though it has some errors and has helped me to realize many of my own. I've also been drawing maps as I go to help narrow down identifications and visually clarify geographic references, visiting some of the locations, and incorporating vintage imagery to add some context and depth to the text. I hope you enjoy the translation. If you would like to get in touch with comments, corrections or suggestions then you are welcome to . The translation began in Feburary 2015 and is progressed through 2018 to about 90% completion, at least as far as a 'first run' translation plus 10% of the secondary process of reviewing the translation against the previous English translation by Gordon H. Luce. This worryingly but thankfully turned up some interesting errors in the digital source text. I took field trips to some of the areas discussed, flew to Japan to try to look at a related source (but the 'museum' was locked and locals had no idea of how to gain access or if it was ever open!), and plan to do another field trip as well. I sure hope someone gets some use out of all this work! Unfortunately some Wiki person decided to come and delete all of the maps on the basis they did not believe backgrounds topography was fair use. These were a cornerstone of the text and are now lost until I can obtain once more access to the original data which is in another country. It is doubtful if this will occur in the near term. Thus, the heart of the translation has been destroyed. I am so deeply saddened by this that I cannot continue. However, it is heartening that multiple people did contact me with sympathy and support. In 2022 I was made aware of not one but two new translations, after this work. The first is also a 2022 creative commons licensed translation by Mr. Ludwig M Brinckmann of Yunnan Explorer which credits this translation and adopts a similar mode of presentation, and the second is a translation by the late Professor Bu Shaoxian of Dali University which was apparently completed in 2015 but only published in 2018. Therefore, should you be a scholar interested in furthering your knowledge of the text, those are probably excellent resources and no doubt in many ways superior to my own imperfect and now tragically cartographically eviscerated efforts. Editions of the original. The real original is lost. The existing editions are partial or recompilations. notes at least eight editions of the source text. We are using a digital version (complete with its own newly introduced errors!) derived from the "Wu Ying (Palace Museum Library) Jewelled Edition"《武英殿聚珍版》. The transliterators of that version often refer to a Nanzhao chapter in the "History of the Later Tang Dynasty" 《新唐書》, which has some similar content but with which nontrivial semantic differences exist, and generally appear to trust these as well-reasoned corrections. They also draw corrections from "Comprehensive mirror to aid in government"《資治通鑒》, "Book of the Eight Zhao" 《八詔篇》 and other sources. The existing (Gordon H. Luce) English translation is also based on the "Complete Library of the Four Treasuries"《四库全书》 — as well as a second source text only identified in broken Wade-Giles Pinyin as "Chien-hsi-ts'un-she ts'ung k'o" (from which a previous, lost translation was made by the same author). It is assumed that the French and Vietnamese versions are also most probably based on one of the first three. Differences with Gordon H. Luce's Previous translation. As well as specific differences in translation, this translation has the following benefits.

Bikol/Singular and Plural Nouns. In Bikol, the plural form of nouns is often indicated by adding the word "mga" before the noun. "Mga" is a specific plural marker. It is pronounced as "mah-NGAH". Example Using numbers or other words expressing quantity.</br> Example

Cherokee/Tone. Cherokee Tone Explained in One Minute. By default, all Cherokee vowels have a low flat pitch. Three other types of pitch, or tones, each used for different reasons, can change the default tone. They are (1) the lowfall tone, arising from the use of pronouns, which starts at the default low and goes lower (glides to a lower pitch); (2) the superhigh tone, which starts high and rises higher, associated with adjectives and dependent clauses; and (3) the high tone, associated with a word's internal stress, whether arising as a core component of the word or from an attached prefix. Tones, grammar rules, and affixes interact within a word in a largely predictable yet intricate system. Cherokee Tone in More Detail. One cannot comprehend the beauty of Cherokee tone without first understanding its canvas. The basic unit of time in Cherokee is the mora, which is the length of any syllable containing a short vowel. If a syllable contains a long vowel, it is two moras long. Some tones (i.e. types of tones) in Cherokee last for one mora, and others for two moras. Yet others last for the whole syllable, regardless of its mora number. This property—which unit of time a type of tone is associated with—is a tone's "domain." The domain can be one mora, two moras, or one syllable. One must also think of the Cherokee tonal system as having one default tone, a low tone with a domain of one mora, that is automatically realized on every syllable unless a "marked" tone is required to be realized there instead. A marked tone arises in Cherokee speech or is changed (e.g. through pitch alternation or spreading) for one of two possible reasons: (1) the word or word part (morpheme) inherently has a certain tone or tone process that surfaces as a marked tone; or (2) a grammatical or phonological feature requires a tone or tone process resulting in a marked tone. To understand the entirety of Cherokee tonology, one needs to understand the intricacies of each type of tone or tone process, including its causes or uses, realization, conditions for change, and effects on coincident and neighboring tones. Every tone or tone process is unique in behavior, which is one reason learners have historically found Cherokee tone so challenging. However, if you look at this fact as testament to the richness of the language and "embrace" the intricacies of the tonal system, you will have a much better learning experience. True Tones. The below table identifies the four true tones of Cherokee. They are called "true" tones because they arise directly from their sources. This is in opposition with "surface" tones, which represent additional tone contours arising from the interactions between true tones or from other downstream processes, such as spreading. The pitch numbers indicates the approximate height of pitch in the realization of the tone on its domain. For example, a low tone is given the (somewhat arbitrarily) pitch assignment of 2. For a single mora, "2" is appropriate. For a low tone on two moras (on a long vowel), "22" is written to indicate the length. For the lowfall tone, "21" is written to indicate that the pitch starts at the same level as called for by a low tone but then glides lower. All notations use the vowel /a/ for demonstration. The first element of the pair indicates the notation for a short (or in the case of lowfall, "underlyingly" short) syllable with the specified tone, while the second element is for a long syllable. Surface Tones. Below are a list of surface tones in Cherokee. These are "results" of the application and interaction of true tones and do not arise alone from meaningful sources. Inert Tones. Some suffixes (and maybe other morphemes) carry high tones that cannot be attributed to one of the three high tone types discussed here. This may also be the case for non-derived nouns. They are “inert” tones because they do not change. H1, H2, and H3 are specific to verbal morphology—so you won’t confuse them with the inert tones—but they are “active” in the sense that they move around or are realized differently in different circumstances. The learner should simply memorize the inert tones as they come up—they are essentially fully lexical. Tonicity. Tonicity is an important concept in Cherokee tonology. It is a property that applies to every inflection of a verb, every noun, and every adjective. Whether a form is "tonic" or "atonic" depends on certain factors. Firstly, why does tonicity matter? Tonicity governs the realization of two types of tone: lowfall associated with pronominal tonic lowering (PTL), and the major high tone (H1). If an inflection is tonic, then any lowfall resulting from PTL will be realized (i.e. present), and any major high tone H1 not otherwise limited will be realized high. If an inflection is atonic, then any lowfall resulting from PTL will "not" be realized, and any H1 will be realized low. Tonicity is determined for an inflection by following this order of questioning: This is to say, at the most basic level, indicative inflections are tonic, as opposed to imperative or infinitive inflections (or nouns and adjectives). However, if any prepronominal prefixes are used in the inflection, the "determination of tonicity defers to them". The only higher power in tonicity determination is the presence of the -vv́qi suffix, which automatically makes an inflection tonic, even if it has a detonicizing PPP, for instance. Also, note that questions 2 and 3 cannot be flipped in order. That is to say, in the presence of both tonicizing and detonicizing PPPs for any particular inflection, tonicizing trumps detonicizing. A flowchart lays out these rules more clearly. Sources of Tones: An Overview. Lowfall from Pronominal Tonic Lowering (PTL). Pronominal prefixes starting with a vowel will see a lowfall on that initial vowel in a tonic environment. If the underlying vowel is short, the lowfall lengthens it. Remember that this, though, is a general property of the lowfall and not unique to this particular source. When PTL occurs concurrently with a high tone, it seems that a falling tone results. This high tone may be H1 or H3. (H2 would not be able to collide with PTL due to location restraints.) Lowfall from Long-i Prefixes. Regardless of tonicity, pronominal prefixes beginning with a long /i/ "always" see a lowfall on that vowel. "Always" refers to the fact that tonicity does not govern the realization of this lowfall. Note that in tonic environments this lowfall will appear identical to PTL. One could argue that in tonic environments PTL also applies on top of the long-i-derived lowfall, which is intuitively true, but this fact does not affect any surface pitch movement, for supposedly the double application of a lowfall simply results in a lowfall—there is no distinct "double" lowfall realization. Lowfall from Laryngeal Alternation (LA). The laryngeal grade of most /hC/ clusters, where C represents any nonglottal consonant, is the disappearance of the /h/ and concurrent assignment of a lowfall tone on the preceding vowel. See the Laryngeal Alternation article for more details about this process. Superhigh on Adjectives. All adjectives carry a superhigh tone.

Bikol/Family. Bikol, like most Austronesian languages, is gender-neutral. The third-person pronoun "siya" is used for both "he" and "she", as well as "it" in the context of being a neuter gender. Native nouns also feature this characteristic, normally with the addition of lalaki ("male") or babayi ("female") to the noun to signify gender in terms such as aking lalaki ("son") or babaying kanding ("she-goat"). However, because Bikol has had over three centuries of Spanish influence, gender is usually differentiated in certain Spanish loanwords by way of the suffixes -a (feminine) and -o (masculine). These words mostly refer to ethnicities, occupations, and family. Some examples are: Bicolana/Bicolano (Filipina/o) and their derivative nicknames Pinay/Pinoy, tindera/tindero (vendor), inhinyera/inhinyero (engineer), tita/tito (aunt/uncle), manang/manong (elder sister/brother), and lola/lolo (grandmother/grandfather). A few gender-differentiating pairs originate from Chinese, mostly relating to kinship terminology such as ate (big sister) and kuya (big brother).

Manksi. Manksi is an auxlang for all of India's languages. It is written in the Latin script but also occasionally in the Devanagari script. The language is combined with all of the official languages in India, making it the most easiest language for speakers of any indian language. Contents. Manksi/Lesson 1 - Greetings, introductions, and goodbyes. Manksi/Lesson 2 - Identifying objects. Manksi/Lesson 3 - Asking directions. Manksi/Lesson 4 - Ordering food. Manksi/Lesson 5 - Review #1

Manksi/Lesson 1. Vocabulary. Amasa - hello Alavida - goodbye annjae - me annjaeni - my aapma - you aapmani - your naan - name haiya - is tumu - what Introduction. Manksi is very similar to English, and the grammar is relatively easy. Conversation. Amasa! Amasa! Aapmani naan haiya tumu? Annjaeni naan haiya John! Annjaeni naan haiya Micheal. Alavida! Question particle. If you are familiar with chinese, tumu is exactly like the word 什么, for those who are not familiar, let’s put an answer. “Your name is ___”, now, to ask “What is your name”, you would say “Your name is what”, which basically says, “Annjaeni naan haiya tumu?”.

Manksi/Lesson 2. Vocabulary. it = this ana = that medi = table kampyootar = computer pensil = pencil apil = apple kuka = dog billa = cat mar = and kee = plural marker Conversation. it haiya tumu? it haiya medi. mar ana haiya tumu? it haiya kukakee mar billakee. Plural marker. The plural marker is “kee”, it is referring to two or more things.

Manksi/Lesson 3. Vocabulary. kahaa = where bana = left seeda = straight ahead sahi = right MakDonald = McDonalds restorena = restaurant ya = or Conversation. MakDonald haiya kahaa? MakDonald haiya bana? Restorena haiya kahaa? Restorena haiya seeda ya sahi. Where? Kahaa has the same grammar purpose for “tumu”, expect it’s for where. Articles. there is no “the” or “a”, this is what makes it the most easiest language for indians.

Manksi/Lesson 4. Annjae = I/me caahani = want postissh = try pala = fruit sabzee = vegatable salaad = salad bargar = burger hyamabargar = hamburger cheezabargar = cheeseburger cammach = spoon zaroora = need illai! = no! cappita = not prati = to (eg. You don’t need a spoon to eat a hamburger.) khao = eat (verb) dyanavaad! = thank you! shuk! = thanks (informally) Conversation. Annjae caahani postissh bargar. Hyamabargar ya cheezabargar? Hyamabargar, dyanavaad! Illai! Annjae caahani postissh hyamabarger! cappita cheezabargar! Cammach haiya kahaa? Illai! Annjaeni cammach! Illai! Aapma cappita zaroora cammach prati khao bargar!

WikiLang/Writing systems. A writing system is a method of visually representing verbal communication based on a script and an orthography or set of rules regulating its use.

Inclusive Data Research Skills for Arts and Humanities/Section 1. Data Skills Session Facilitators: Gauti Sigthorsson (U. of Roehampton), Carla Fernandez (UAL), Harry Solomons (UAL, LCC) = Session Summary = This session supports the overall aim of today's event, to co-create inclusive data skills assets and materials for arts and humanities researchers with Wikimedia UK and the DAReS project team.  The theme of this session is "What works for Arts & Humanities practitioners when working with data?" Therefore, we aim to explore arts and humanities-specific approaches to data, tools, methods and research outputs. Facilitators will work with the following themes as starting points. Participants are welcome to move between groupings as they wish: What counts as data in arts and humanities? Facilitator: Harry Solomons. Focusing on the distinction between qualitative and quantitative data, this session unpacks what counts as data in the arts and humanities, including quantitative and qualitative data, as the most readily apparent form of data in social sciences. Case studies, data sources and primary/secondary data considered, along with specific case studies. How can you tell stories with data? Facilitator: Carla Fernandez. Focusing on data visualisation as both a research method and a practice, this session focuses on the route to exploration, discovery and the storytelling of information. Explore, Discover, Explain. Data Practices Facilitator: Gauti Sigthorsson. This session focuses on key data skills questions to connect arts and humanities projects or topics to specific tools and methods External link to Google Doc: Document for drafting

Inclusive Data Research Skills for Arts and Humanities/Section 2. Session plan: Data epistemologies and decolonial approaches. Brief session description: One of the challenges we face as arts and humanities researchers who want to engage with data-oriented computational tools and methods is that these are usually embedded within scientific as well as colonial disciplinary priorities and biases which can be very hard to unpick and work with in critical research. In this session, we focus on asking: What kinds of thinking, epistemologies and colonial practices come with data tools, skills and methods? How can we engage decolonial approaches to data for arts and humanities? How might we move towards empowering forms of data research? To meet these challenges we will work in small groups around these specific related problems: Sub-themes: Each of these groups will engage these questions and work to identify what these challenges look like in research and their experience. The aim is to better understand these areas and co-create collaborative resources for sharing across Arts and Humanities and with under-represented groups.

Inclusive Data Research Skills for Arts and Humanities/Section 3. Session plan: Hacking the Research Journey. Brief session description. A core challenge for arts and humanities researchers interested in pursuing data-oriented research is finding ways to navigate their research pathways. During the DAReS project thus far, the research journey was important for both inclusion and data skills. This includes challenging the structural inequalities that limit arts and humanities researchers’ access to data skills but also figuring out ways to plan, design and identify sources of funding. This session critically engages with these challenges, hacking the research journey itself, the solutions to common problems, with the objective of forming a self-sustaining network of arts and humanities researchers potentially co-launching projects. The objectives of this section were to:

Inclusive Data Research Skills for Arts and Humanities/Section 4. Feedback and Reflection During the Hackathon our wonderful IT support (Iza) will be hosting a feedback and reflection booth. This will be a place to stop and leave a note about the day, the project or the wiki book. You can use paper and pens, leave a video or audio note or log on to our padlet and leave a comment digitally. Even if you just want to say ‘Hi’. Padlet for reflection and feedback

Inclusive Data Research Skills for Arts and Humanities/Agenda for the Day. Inclusive Data Research Skills Hackathon for Arts and Humanities (DAReS). Thank you for signing up to our Hackathon on Friday 26th January 2024. We look forward to welcoming you on the day, and to collaborating on co-creating a Wikibook on inclusive data skills and materials for arts and humanities researchers with Wikimedia UK and the DAReS project team. You can find out more about the DAReS project here Agenda

Antenna Television/Market Listings/Arkansas. Fort Smith-Fayetteville. Counties. Arkansas Oklahoma

Bikol/Grammar. Word connectives or “ligatures” are a unique part of the Bikol language that are used to link two words together. There are a variety of grammar patterns that require words to be connected by ligatures. The first pattern to learn, is that you should connect nouns and the adjectives that describe them using ligatures. For example, in the phrase: “beautiful maiden”, the words “beautiful” and “maiden” should be connected with a ligature in the Bikol language. Two types of ligatures: 1.) If the first word ends in a vowel, the ligature -ng is attached to the end of that word to connect it to the next word. Example: daragang magayon "(beautiful maiden)" 2.) If the first word ends in any consonant including the letter "N", the word "na" is used to connect two words. Example: magayon na daraga "(beautiful maiden)" Quiz: 1.) halangkaw + harong "(tall house)" 2.) kamot + maati "(dirty hand)" 3.) turog + ikos "(sleeping cat)" 4.) naglulukso + aki "(jumping child)" 5.) mayo + kwarta "(no money)"

Chess Opening Theory/1. e4/1...c6/2. d4/2...d5/3. Nc3/3...dxe4/4. Nxe4/4...f6. =Caro-Kann f6= This has been tried quite a few times, but chances of success are low. Epilogue. The f6 Caro-Kann is a rare line that weakens the kingside and hinders the development of the king's knight, but prepares an e5 push.

Chess Opening Theory/1. e4/1...c6/2. d4/2...d5/3. Nc3/3...dxe4/4. Nxe4/4...f6/5. Ng3. =Caro-Kann f6= This refutes the f6 line. Epilogue. Ng3 is an interesting but important move that allows the e5 pawn push, but gains a huge lead in development after 5...e5 6. Nf3.

Chess Opening Theory/1. e4/1...c6/2. d4/2...d5/3. Nc3/3...dxe4/4. Nxe4/4...Bf5/5. Ng3/5...Bg6/6. h4/6...h6/7. h5. =Caro-Kann= This move attacking the bishop again is inferior due to it wasting two tempi on moving the h-pawn. Nimzowitsch Variation: 7...Bh7 8. Rh4!? The Nimzowitsch Variation is a rare line of the Caro-Kann that develops the rook early but creates some complications for black.

Chess Opening Theory/1. e4/1...c5/2. Nf3/2...d6/3. d4/3...cxd4/4. Nxd4/4...Nf6/5. Nc3/5...g6/6. f4. The idea of this sneaky move is to setup a tactic that wins a knight if black's not careful. Main Line - 6...Nbd7. The best move and the refutation. Black develops the other knight and supports the other knight. The Trap - 6...Bg7?? 7. e5! If black goes for the normal dragon theme, they lose a tempo with. e5! as the move 6. f4, in fact prepared e5.

Chess Opening Theory/1. e4/1...c5/2. Nf3/2...d6/3. d4/3...cxd4/4. Nxd4/4...Nf6/5. Nc3/5...g6/6. f4/6...Bg7. Black fell for the trap of 7. e5!.

Chess Opening Theory/1. e4/1...c5/2. Nf3/2...d6/3. d4/3...cxd4/4. Nxd4/4...Nf6/5. Nc3/5...g6/6. f4/6...Bg7/7. e5. White still isn't winning the knight, but "trading pawns" is met with that fate!

History of Economic Thought/Introduction. The history of economic thought is a fascinating journey that spans centuries, weaving through the intellectual landscapes of different cultures and eras. At its core, economic thought explores the ways societies allocate resources to meet their needs and desires. Our journey begins in ancient times when philosophers like Aristotle contemplated economic principles. Aristotle, in his work "Politics," delved into the concept of oikonomia, focusing on household management and the exchange of goods. Fast forward to the 18th century, and we encounter the Scottish Enlightenment thinkers, notably Adam Smith. In his seminal work, "The Wealth of Nations," Smith laid the groundwork for classical economics. He introduced the invisible hand concept, arguing that individuals pursuing their self-interest unintentionally contribute to the overall well-being of society through the market mechanism. The 19th century witnessed the rise of various economic theories. Karl Marx, a German philosopher and economist, developed the theory of communism, critiquing capitalism for its inherent class struggles. Concurrently, neoclassical economists like Alfred Marshall emerged, emphasizing the role of supply and demand in determining prices and resource allocation. The Great Depression of the 1930s spurred the development of Keynesian economics, named after John Maynard Keynes. Keynes advocated for government intervention to manage economic downturns, challenging the laissez-faire approach. His ideas gained prominence, influencing policies during the post-World War II era. The latter half of the 20th century witnessed the rise of monetarism, led by economists like Milton Friedman. Monetarists argued for controlling inflation through the manipulation of the money supply, challenging Keynesian orthodoxy. This period also saw the emergence of behavioral economics, incorporating insights from psychology to understand economic decision-making. As we step into the 21st century, new economic paradigms continue to emerge. Development economics focuses on addressing global inequalities, environmental economics grapples with sustainability, and behavioral finance explores the psychological factors influencing financial decisions. The digital age has given rise to discussions about the gig economy, automation, and the impact of technology on traditional economic structures. The evolution of economic thought continued to unfold in the 20th century with the advent of new theories and perspectives. The aftermath of World War II saw the establishment of the Bretton Woods system, named after the conference that took place in 1944. This system set the stage for the International Monetary Fund (IMF) and the World Bank, institutions designed to stabilize and reconstruct the global economy. During this period, the neoclassical synthesis emerged, integrating Keynesian ideas with neoclassical economics. Economists like Paul Samuelson played a pivotal role in reconciling the seemingly disparate views, emphasizing the importance of both market forces and government intervention in maintaining economic stability. As the 20th century progressed, the focus shifted towards development economics. Scholars like Rostow and Prebisch explored the dynamics of economic growth in developing countries, highlighting the role of industrialization and the challenges posed by unequal global trade. The 1970s marked a turning point with the rise of supply-side economics. Influenced by thinkers like Arthur Laffer, this perspective argued for reducing tax rates to stimulate economic growth. Concurrently, the oil crisis and stagflation challenged existing economic paradigms, leading to a reevaluation of policy approaches. The latter part of the 20th century also witnessed the emergence of environmental economics as a distinct field. Concerns about resource depletion, pollution, and climate change prompted economists to incorporate environmental considerations into economic analyses. This led to discussions about sustainable development and the need for balancing economic growth with environmental preservation. The collapse of the Soviet Union in 1991 marked the end of the Cold War and a shift in economic ideologies. The triumph of market-oriented reforms and the spread of globalization became defining features of the late 20th century. Institutions like the World Trade Organization (WTO) gained prominence, advocating for free trade and economic liberalization. Entering the 21st century, the global financial crisis of 2008 prompted a reassessment of economic models. Questions about the stability of financial markets and the role of regulatory frameworks gained renewed attention. Economists like Joseph Stiglitz and Paul Krugman contributed to discussions about the implications of the crisis and the need for reform. In recent years, the digital revolution has introduced new dynamics into economic discussions. The gig economy, characterized by temporary and flexible employment, challenges traditional labor market models. Issues of income inequality, technological unemployment, and the impact of artificial intelligence on work are subjects of ongoing debate within the field of economics.

Open Scholarship Press. "The Open Scholarship Press makes relevant open social scholarship research and output available openly to academics and non-academics alike." Its mandate is threefold: The Open Scholarship Press has published foundational research scans and curated volumes of reprinted, open access material on PubPub and Wikibooks. Further details are available here. Open Scholarship Press Collections. The Open Scholarship Press Collections feature four individual, book-length annotated bibliographies with analytical overviews covering key areas of open social scholarship: Community, Connection, Policy and Training. Open Scholarship Press Curated Volumes. The Open Scholarship Press Curated Volumes feature four individual primers, book-length curated volumes of essential readings, following an analytical introduction, covering key areas of open social scholarship: Community, Connection, Policy and Training. Other Publications Aligned With and Supporting OSP Volumes. These related publications have been part of the research and design process associated with these Open Scholarship Press volumes.

Chess Opening Theory/1. d4/1...Nf6/2. Bg5/2...Ne4/3. Bc1. =Trompowsky Attack= Bc1 is a rarely seen retreat that undevelops the bishop and allows black to get easy play.

Chess Opening Theory/1. d4/1...Nf6/2. Bg5/2...Ne4/3. Bf4. =Trompowsky Attack= Bf4 is the normal retreat that we normally see.

Chess Opening Theory/1. d4/1...Nf6/2. Bg5/2...Ne4/3. Bf4/3...g5. =Trompowsky Attack= g5 is a dubious one move attack of the bishop that weakens the kingside and allows the Bf4 to sit on a strong outpost on e5.

Inclusive Data Research Skills for Arts and Humanities/About the Project. The DAReS project The DAReS project is a 12 month project (April 2023-March 2024) that came to be in response to systemic inequalities in many academic related sectors (e.g. tech and computing, higher education, and research) and how these are amplified at the intersection of arts and humanities research, data and digital skills, and research, especially for under-represented groups. The key question for us was not ‘why are under-represented groups not engaging data skills?’ but ‘why are there so few approaches prioritising inclusion’ and ‘what does an inclusive approach look like?”. Thus, rather than developing a skills training programme for under-served groups, the DAReS project aimed to work with such groups to co-design an inclusive data skills curriculum. Taking an ethical co-creation approach, the DAReS project aims to develop an arts and humanities focused and inclusive data and digital skills curriculum which can inform a scalable regional or national pilot. This Hackathon comes in the last phase part of the DAReS project, which is funded by the AHRC as part of the IDAH Digital Skills Network, lead by London College of Communication, University of the Arts London, with the Creative Computing Institute, King’s College London, Leeds University, Roehampton University, and partners, CRAC/Vitae and Wikimedia UK. The DAReS project is officially titled ‘Transforming the Gap: Inclusive Digital Arts and Humanities Research Skills’ (DAReS), AHRC AH/X007510/1. https://www.arts.ac.uk/colleges/london-college-of-communication/research-at-lcc/dares

Inclusive Data Research Skills for Arts and Humanities/DAReS Codesigners. Co-Designers. The DAReS co-designers are at the heart of this project and their thoughtful, engaged contributions have pushed the boundaries of how inclusion and arts and humanities focused data and digital research skills can be understood. This project would not exist without them and with thanks to each of them, they are:

AQA A-Level Physics/Longitudinal and transverse waves. Longitudinal and transverse waves are the two main classifications/types of waves. Transverse Waves. Transverse waves are characterised by the fact that the direction of energy transfer is perpendicular to the direction of wave oscillation and particle displacement. Examples of Transverse waves include the whole EM Spectrum, secondary earthquake waves, and waves formed along a string. It is also important to note that all EM waves travel at the same speed in a vacuum, that being 3.00*10^8ms^-1 Most transverse waves cannot travel through liquids. While oceanic waves themselves are transverse, these waves instead propagate through the surface tension of the ocean. Light and EM waves are also 'self propogating', meaning they sustain themselves using their own magnetic field, and can therefore travel through liquids. This self-propogation concept is not assessed in any of the Physics A-Level, however it may help with your understanding of transverse waves. Polarisation of Transverse Waves. Transverse waves can also be polarised. Polarisation is the act of restricting the vibration of a transverse wave to one vertical plane / direction. This can, in many cases where a pulse of light will consist of many different individual waves oscillating in different directions, reduce the amount of perceived light that would pass through the polarising filter. Therefore, polarising filters are used in various everyday applications, such as; This feature of polarisation is exclusive to Transverse Waves. Therefore, in scientific applications, polarising filters can be used to easily and effectively determine the type of wave when this is not a known quantity. Longitudinal waves. Longitudinal waves are characterised by the fact that the direction of energy transfer is parallel to the direction of wave oscillation and particle displacement. Examples of Longitudinal waves include sound waves, primary earthquake waves, and ultrasound waves. Longitudinal waves cannot be polarised.

Stellar Astrophysics/Energy Transport. Within a star there are two main methods of energy transport: radiation and convection. Both of these take place within our Sun, but occur in different regions of the star. Modelling this transfer is important understand how stars function and evolve over time. Radiative Transfer. Radiative transfer is the transfer of energy via photons, and is the dominant method of transport within the core of the Sun and in the solar atmosphere. Photons passing through a region of gas can be absorbed while other photons may be emitted from the same region, so to model radiative transfer one must consider both the absorption and emission of radiation. Parallel ray equation. In this section, we assume that the light is emitted in parallel rays to the observer. The more general non-parallel case will be considered later. Absorption. Imagine there is a column of gas with a side area of formula_1 and length formula_2. Assume that the gas has a number density of formula_3 absorbing particles per unit volume, with each particle having an effective cross-sectional area formula_4 when interacting with light of a particular wavelength. From this, the proportion of area A that blocks the light is:formula_5This dimensionless value is called optical depth, and is proportional to the amount of light absorbed. The number density can be rewritten in terms of the mass density formula_6 and average molecular mass formula_7 as: formula_8, which allows the above expression to be written as formula_9. formula_10 is also known as the mass absorption coefficient, or the opacity per unit mass. From the definition of optical depth, it can be implied that the reduction in light intensity caused by absorption is: formula_11 Therefore, if only absorbtion is taking place, the intensity of the light leaving the column can be determined by integrating over the length of the column: formula_12formula_13formula_14

Chess Opening Theory/1. d4/1...g5/2. Bxg5. =2. Bxg5: Borg Gambit Accepted= First Impressions. After accepting the Borg Gambit with Bxg5, White has gained a pawn as well as developed the queenside bishop. Obviously, this puts Black at a disadvantage, as White is now leading in development. Black has three main options here: Play the better move Bg7 and commit to a pseudo-Indian defense, c5 to try to destabilize the center and let the queen out, or try to kick the bishop out with f6. 2... Bg7. Bg7 prioritizes development over central control, and is considered better by masters and computers alike. This move aims to get quick development up in exchange for gambiting the g pawn. White can fight back by expanding in the center and developing their own pieces, leading to a rather quick race for who can develop their pieces the fastest. This gambit is usually considered not justified. 2... c5. The intent of c5 has two reasons. First, it is to destabilize White's center pawns and force them to respond lest they lose space by a center pawn being taken by a flank pawn. Secondly, it is to let the queen out, which can help destabilize the center or hunt the b2 pawn. This also develops a piece, getting a bit of advantage back for Black. 2... f6? f6 attempts to kick away the bishop and expand in the center with an eventual e5, but this can be refuted with 3. e4!, saving the bishop for 1 turn and making some center space. If Black tries to take the bishop, the White queen can go to g5 for a quick checkmate. Overall, this line is considered inferior to Bg7 by masters and computers. Other options. Black can also play Nf6 to develop the kingside knight, however after Bxf6, there are doubled pawns on the f file and there is a clear line of sight to the king which can be exploited since the kingside pawns are weak. Theory table. 1.d4 g5 2.Bxg5

Inclusive Data Research Skills for Arts and Humanities/About the DAReS project. The DAReS project is a 12 month project (April 2023-March 2024) that came to be in response to systemic inequalities in many academic related sectors (e.g. tech and computing, higher education, and research) and how these are amplified at the intersection of arts and humanities research, data and digital skills, and research, especially for under-represented groups. The key question for us was not ‘why are under-represented groups not engaging data skills?’ but ‘why are there so few approaches prioritising inclusion’ and ‘what does an inclusive approach look like?”. Thus, rather than developing a skills training programme for under-served groups, the DAReS project aimed to work with such groups to co-design an inclusive data skills curriculum. Taking an ethical co-creation approach, the DAReS project aims to develop an arts and humanities focused and inclusive data and digital skills curriculum which can inform a scalable regional or national pilot. This Hackathon comes in the last phase part of the DAReS project, which is funded by the AHRC as part of the IDAH Digital Skills Network, lead by London College of Communication, University of the Arts London, with the Creative Computing Institute, King’s College London, Leeds University, Roehampton University, and partners, CRAC/Vitae and Wikimedia UK. The DAReS project is officially titled ‘Transforming the Gap: Inclusive Digital Arts and Humanities Research Skills’ (DAReS), AHRC AH/X007510/1. More information is on the DAReS web page: https://www.arts.ac.uk/colleges/london-college-of-communication/research-at-lcc/dares.

Inclusive Data Research Skills for Arts and Humanities/Project Team. The project team consists of an interdisciplinary team with the following roles: Principal Investigator Co-Investigators Post-Doctoral Research Fellow Project Coordinator Senior IT Support Analyst Project Partners

Inclusive Data Research Skills for Arts and Humanities/Contributors. Although Wikibooks has its own mechanisms for keeping track of editors and contributors, we warmly invite all Hackathon participants to add their name below in recognition of their work and contribution. You are also welcome to add your role and institutional affiliations(s) if you wish.

Inclusive Data Research Skills for Arts and Humanities/Purpose and context of code. The DAReS project set out to work with marginalised arts and humanities researchers at every career stage to develop and pilot an inclusive model for advanced data and digital research skills provision. The longer term goal is to transform known gaps in data and digital skills research training by moving away from a focus on individually oriented ‘skills literacies’ and towards the lack of inclusive approaches in arts and humanities data skills training. To achieve these aims, we recruited 30 co-designers who identified with under-represented groups with protected characteristics as identified by the 2010 Equalities act (black, ethnic minorities, those who are part of national or migration status minorities, part of the LGBTQ+ community, those with disabilities, neurodiversity (or neurodiversities). We set out a programme of co-design work on inclusion and arts and humanities based data and digital research skills. In this work, we prioritised the ethical co-creation of materials, trust building and a learning environment shaped by inclusion and openness. Part of this work involved co-creating a code of conduct for the 12 month project specific to our community and through which we could articulate norms and share values and expectations. This code of conduct has been adapted from this work specifically for the Hackathon. It includes contributions from everyone in the DAReS project and in our view, represents what we hope is both good inclusive practice and an on-going inclusive process - intended to build in and on a culture of respect. These are articulated in values, clear points on inclusive behaviours we ask all participants to strive for, and a commitment to avoid any behaviour that is prejudicial, biassed, or discriminatory. As part of a co-design project, this is also an evolving code of conduct and one that can be shaped by those who want to shape its future development.

Inclusive Data Research Skills for Arts and Humanities/Purpose, values, code and consequences. DAReS Code of conduct. Purpose and context of the code. The DAReS project set out to work with marginalised arts and humanities researchers at every career stage to develop and pilot an inclusive model for advanced data and digital research skills provision. The longer term goal is to transform known gaps in data and digital skills research training by moving away from a focus on individually oriented ‘skills literacies’ and towards the lack of inclusive approaches in arts and humanities data skills training. To achieve these aims, we recruited 30 co-designers who identified with under-represented groups with protected characteristics as identified by the 2010 Equalities act (black, ethnic minorities, those who are part of national or migration status minorities, part of the LGBTQ+ community, those with disabilities, neurodiversity (or neurodiversities). We set out a programme of co-design work on inclusion and arts and humanities based data and digital research skills. In this work, we prioritised the ethical co-creation of materials, trust building and a learning environment shaped by inclusion and openness. Part of this work involved co-creating a code of conduct for the 12 month project specific to our community and through which we could articulate norms and share values and expectations. This code of conduct has been adapted from this work specifically for the Hackathon. It includes contributions from everyone in the DAReS project and in our view, represents what we hope is both good inclusive practice and an on-going inclusive process - intended to build in and on a culture of respect. These are articulated in values, clear points on inclusive behaviours we ask all participants to strive for, and a commitment to avoid any behaviour that is prejudicial, biassed, or discriminatory. As part of a co-design project, this is also an evolving code of conduct and one that can be shaped by those who want to shape its future development. Values. Patience, Hope, Sharing, Idealism, Compassion, Desire for change, Diversity, Humble, Understanding, Equality, Contribution, Kindness, Innovation, Thoughtfulness, Openness, Inclusivity, Equitable, Interdisciplinary, Empathetic, Creative, Diverse, Social purpose, Understanding complexity, Celebration, Inclusion. We ask that you agree to the following:. 1. Provide allyship and advocacy: Support and stand up for others, especially marginalised groups, to foster inclusivity and equality. 2. Empathise before communicating: Seek to understand others' perspectives before responding. 3. Avoid talking over people: Respect others' contributions and avoid interrupting during discussions. 4. Pause for thoughts: Take time to consider responses instead of impulsively reacting. 5. Negotiate meaning: Clarify and ensure mutual understanding during discussions and conversations. 6. Practise active listening: Fully engage in listening and show genuine interest in others' thoughts. 7. Encourage open and honest communication: Foster an environment where people feel comfortable sharing their thoughts honestly. 8. Consider experience and perspectives: Acknowledge and value diverse experiences and viewpoints. 9. Ask open questions: Where relevant, encourage open communication and knowledge exchange, by asking questions that promote open-ended responses. 10. I will not behave in a way that discriminates, offends, intimidates and/or is hostile, degrading or humiliating to others. This includes jokes and banter as well as physical actions related to harassment and/or discriminatory behaviours. Consequences for violating the code. The process for enforcement is dependent on the gravity of the behaviour. If a participant engages in a discouraged behaviour, the team retain the right to take actions to keep the event a welcoming environment for all participants. This includes warning the offender or expulsion from the room/event if necessary. Reporting. If someone makes you or anyone else feel unsafe or unwelcome, please report it as soon as possible (in person or using the channels available, see below). When reporting please be specific, stay constructive, and use respective communication. Information on how to report anonymously can be found in the Hackathon welcome pack or you can report via email [email protected].

Inclusive Data Research Skills for Arts and Humanities/Data inequalities and power. What is power? Contributor 6: Power is with policymakers at the government level and academic funders What is the challenge? Challenge Structure:. Defining challenges What is the context? Examples and sources Challenge 1: Chinese queer culture study Government Censorship: The Chinese government tightly controls information, particularly on topics deemed sensitive or politically controversial. Research on queer issues may be subject to censorship, with certain topics or findings deemed unacceptable or politically subversive. Chinese social media regulation of queer, "THE GENERAL RULES", restrict all queer-related mediated posts in China, which means if you have queer-related posts, you will be banned. Stakeholders: the Chinese government, including the regulatory authority responsible for media and information control. Lack of Official Recognition: China does not officially recognize same-sex relationships, and LGBTQ+ rights are not fully protected under the law. This lack of recognition may limit the availability of data and resources for researchers and contribute to societal stigma surrounding queer issues. Stakeholders: the Chinese government, particularly social media policymakers responsible for enacting laws Social Stigma and Discrimination: Despite some progress in recent years, LGBTQ+ individuals in China still face significant social stigma and discrimination. This stigma can affect research participants' willingness to disclose their experiences or participate in studies, leading to challenges in data collection and interpretation. Stakeholders: Chinese society, including individuals, communities, and institutions that perpetuate social stigma and discrimination against LGBTQ+ individuals. Language and Cultural Barriers: For researchers from outside China, language and cultural barriers can pose additional challenges to conducting research on queer issues in the country. Stakeholders: Researchers, academics, and institutions from diverse linguistic and cultural backgrounds seeking to research LGBTQ+ issues. Challenge 2: Date Access Limitations- stem from diverse factors, including privacy regulations, legal and ethical considerations, proprietary constraints, and technical barriers. - Privacy regulations often require strict controls on the access to datasets containing sensitive information, prompting researchers to navigate compliance intricacies. (Stakeholders: individuals; researchers and entities that responsible for overseeing and ensuring the privacy date and regulations) - Proprietary datasets owned by private entities may have restricted access due to commercial interests, necessitating negotiations or collaborative efforts. (Stakeholders: private Entities, owners of the proprietary datasets in protecting their data) - Some data sources may limit usage to specific purposes, such as academic research, and researchers must respect these conditions. (Data providers, researchers who determining their data usage) - Accessing data through APIs may encounter rate limits, prompting researchers to manage requests responsibly. (Stakeholders; API Providers; IT Teams manage server loads and limit) - Data quality, security, and confidentiality may also contribute to access restrictions. (Date subjects; data providers) Challenge 3: When discussing the challenges that women face in the context of gender and data colonization, particularly in terms of data methods and skills, the challenges are: Stakeholders: government agencies, businesses and tech companies, social media platforms and general public(specifically for women) Challenge 4: Challenge 5: Digitalisation and downloadability hierarchy of resources and data: Challenge 6: Data tools Who are the stakeholders? Funding bodies Licensing softwares Designer of softwares - incorporating languages Institutions and their postgraduate and research bodies that allocate budget for research Tackling the challenges. Collective Bibliography of theoretical frameworks. - Acilar, A. and Sæbø, Ø., 2023. Towards understanding the gender digital divide: A systematic literature review. "Global knowledge, memory and communication", "72"(3), pp.233-249. - Antonio, A. and Tuffley, D., 2014. The gender digital divide in developing countries. "Future Internet", "6"(4), pp.673-687. - Bailer, Savita., 2018 ‘Gender, Mobile, and Development:The Theory and Practice of Empowerment | Introduction’. "Information Technologies". - D'ignazio, C. and Klein, L.F., 2023. "Data feminism". MIT press. - Buolamwini, J. and Gebru, T., 2018, January. Gender shades: Intersectional accuracy disparities in commercial gender classification. In "Conference on fairness, accountability and transparency" (pp. 77-91). PMLR. - Noble, S.U., 2018. Algorithms of oppression. In "Algorithms of oppression". New York university press. - York, J.C., 2022. "Silicon values: The future of free speech under surveillance capitalism". Verso Books. - Benjamin, R., 2023. Race after technology. In "Social Theory Re-Wired" (pp. 405-415). Routledge. - Nyamnjoh, F.B., 2019. ICTs as Juju: African inspiration for understanding the compositeness of being human through digital technologies. "Journal of African Media Studies", "11"(3), pp.279-291. - McFarlane, A., Samsioe, E., 2020. #50+ fashion Instagram influencers: cognitive age and aesthetic digital labours. JFMM 24, 399–413. https://doi.org/10.1108/JFMM-08-2019-0177 - Digital activism e.g. the Risktakers Fellowship hosted by the Allianz Foundation - Youth-focused digital technology training programmes e.g. the Digital Day Camp hosted by Eyebeam - Research Data MANTRA. https://mantra.ed.ac.uk/. - Lehuedé, S. (2024). An alternative planetary future? Digital sovereignty frameworks and the decolonial option. "Big Data & Society", "11"(1). - Gupta, S. (2020). Digital India and the Poor: Policy, Technology and Society (1st ed.). Routledge India. https://doi.org/10.4324/9781003010241 Propose some creative and critical ways in which you can address some of the challenges:. Editor 1: Digital and Online Platforms: Utilize digital and online platforms to conduct research in environments where censorship or restrictions may limit access to traditional research methods. Leverage social media, online surveys, and virtual focus groups to reach LGBTQ+ individuals. Virtualized Sexual Orientation: Conduct design research by blurring the interviewee’s sexual orientation (queerbaiting/straightbaiting). Circumvent government policy and access all interviewees as straight people Editor 2: 1. Collaboration and Networking: developing the connections with institutions, organizations, or individuals who have access to the desired datasets. 2. Participation in Data Initiatives: participating in data initiatives that focus on specific research areas may provide access to shared datasets. 3. Advocacy for Policy Changes: engaging in advocacy efforts for policy changes that facilitate responsible and ethical data access can contribute to a broader shift in the research landscape. 4. Alternative Data Sources: exploring alternative data sources that are more accessible or publicly available can be a pragmatic approach. Editor 3: Gender-Sensitive/Fair Data Collection Training: Provide gender sensitivity training for data scientists, analysts, and relevant professionals to ensure they understand, consider, and appropriately handle gender-related data. Meanwhile, advocate for fair and inclusive data collection methods, ensuring datasets include diverse gender information to avoid stereotypes and biases. Promote Tech Gender Equality: Efforts to narrow the technology gender gap, providing more opportunities for women to engage in digital technology and data science fields. This includes offering training, mentorship programs, and career development support. Encourage Gender Diversity/Data Expertise: Advocate for gender diversity in data science teams, ensuring women are adequately represented in the development of data methods and skills to better address their needs. Especially when conducting research related to women/feminism, having the same-gender researchers are necessary. Also,encourage and support research and educational institutions to conduct professional training and research on gender data to cultivate specialized talent. Develop Gender-Friendly Tech Tools: Encourage and support the development of gender-friendly technology tools, making them more adaptable to women's needs while ensuring the gender neutrality of technological solutions. Digital Literacy and Skills Training: Provide digital literacy and skills training tailored for women to enhance their participation in the digital society, improve understanding of data science, and increase employment opportunities in related fields. Emphasize Privacy Protection: Strengthen privacy protection for women, including ensuring compliance during data collection and processing and assessing risks that may lead to privacy infringements. Which also need to be noticed is the research ethics, the boundaries between personal privacy and research topics have to be carefully discerned from a gender perspective. Editor 4: Editor 5: Editor 6: Resource or tool kit for humanities researchers - the example is https://mantra.ed.ac.uk/ Training and discussions with software designers and policymakers - such as Brandwatch - to keep at forefront inclusivity and diversity of various cultural perspectives to cater global communities.

Inclusive Data Research Skills for Arts and Humanities/Data agencies. Introduction and context. The objectives of this section were to: Core question is: how do we approach data research methods in ways that are empowering for marginalised and excluded groups? "Critical science and decolonial theory, when used in combination, can pinpoint the limitations of an AI system and its potential ethical and social ramifications, becoming a "sociotechnical foresight tool" for the development of ethical AI" (Royer, 2020: 22). Decolonial theory has its foundations in race, law, feminism, queer theory, and philosophical technology studies and understanding the blind spots and limitations of a particular technology necessitates exposing the power dynamics and political relationships that support its application. By infusing a decolonial critical approach into AI, data and socio-technical communities, we could establish insights and approaches that better connect research and technology development to established ethical values grounded in decolonial theory. This will require the development of new research cultures, as well as original technical research in equality and fairness, including the definition of fairness and its ideals, translation, and privacy. Encouraging inclusive dialogue in research methodologies could contribute to the development of a responsible AI and a renewed responsibility to current technologies. By critically engaging with the past and present, researchers must try to unlearn colonial reasoning, reinstate norms of living that were previously incompatible with life, and create new forms of political and affective transdisciplinary research communities to address these challenges. Ontological Turn - Digital and the real. Important distinctions between dualism have been adopted in humanities theory, "A diverse body of work known as the “ontological turn” has made important contributions to anthropological theory. In this article, I build on this work to address one of the most important theoretical and political issues haunting contemporary theories of technology: the opposition of the “digital” to the “real.” This fundamentally misrepresents the relationship between the online and offline, in both directions. First, it flies in the face of the myriad ways that the online is real. Second (and just as problematically), it implies that everything physical is real. Work in the ontological turn can help correct this misrepresentation regarding the reality of the digital" (Boellstorff, 2016: 387). Define how we are using ontology here relating to data and digital -----expand (holbraad, links between decolonial, animism, process phil) Ontology as a way of acting a reality or realities, performing the process, and constructing and acting those life worlds. What we believe our world is made up of, plays a part in how we interact with it, and our understandings of that world (reality/realities) are shaped also by our actions. Data epistemologies. What are data epistemologies? Data epistemologies: How to move past critique and challenge/compliment scientific disciplines (challenge the dualism between coloniser/colonised to redefine decolonisation). Gaps between data literacies (individualised), data infrastructures (e.g. university computing, data tools and developers, disciplinary approaches) and critical data thinking (e.g. data feminism, data conscience, critical data literacies) CARE principles. Data Epistemolgies map Examples of data epistemologies. Nodocenctrism and paranodality (Mejias 2009; Mejias 2013; Barnes 2020) The Alternative Epistemologies of Data Activism (Milan & Velden, 2016) Materiality of networks and data (e.g. Starosielski) Multi-methods and/or other methods: What does data agency look like? Definition. What are Data Agencies? Refers to agency of humans and non-humans. For example, data has agency itself. Which values shape Data Agencies? Where do they come from? How and why? Values will be a crucial issue if we consider social environments/entanglements as a project of mutual creation, cooperatively constructed and rebuilt. How are we defining "agency?" "And how are we prioritizing it (who/what gets priority)? - develop" Data agencies may be regarded multilayered since they interact with a variety of systems at different times (people, social structures, economies, and technical systems), all of which may be examined using diverse approaches. Data agencies possess agency distinct from humans and exert multidirectional influence, representing varying meanings to individuals in different circumstances and times, influenced by the researchers agency and their reflexivity.These agencies can affect both humans and machines (as actors) and create areas of conflict that may generate prospective changes. Data agencies are inherently complex, impacting individuals and data at multiple junctures through feedback loops. If we consider the concept of data agencies as a crucial link between the machine and the human, this situates machine learning technologies as being a key analytical tool for the possibility of being able to decode data agencies. One way to think of data agencies is to consider the humility of the researcher and how the data challenges the researcher. For example, the researcher should be prepared to shift their own thinking and enter into a reciprocal relationship with the data and its agency (as an object of study) in which the researcher is not just trying to explain and interpret the data but allows it to shift you and become a participant within a movement in your own thinking. The agency of the data could lead us to analytical humility and reflexivity while allowing for conceptualisation between the researchers agency and the data's agency as mutual entities inhabiting different worlds (Holbraad et al, 2014). Even if we try to adopt pre-existing frameworks or establish prioritization checklists to support research endeavors, a goal of decolonial data agency is to question methodologies and practices, within reason. There will always be more context that can be provided and support research conclusions, but balancing interpretability and explainability with performance and the ability to execute is as fundamental a part of a research process as it is an AI model. Questions Where is the agency found? How does it interact with the machine and human in symbiosis? Where is the intersection of contact? Agency embedded in the data though historical processes/extension of the human mind but takes on an agency as the data. Data and algorithmic code’s encounter with the human but where are the concrete set of connections between humans and machines? Can we build a system that does not oppose the machine and data/algorithms against the body or being human but is a coevolution and can be decololnised with biases revised? If so, at which point in the process? Where are the intersecting value systems that could define an agreed set of 'fairness' values to work with? Machine learning often superficially scrapes data instead of delving into in-depth analysis------expand. Issues of alignment and values competing values may prevent stakeholders from defining fairness while developing machine learning systems. Where is the intersection of an agreed-upon idea of 'fairness' between distinct value systems (which may be applied to frameworks)? Consider the concept of sameness (not universalism) but a sameness within/ 'inside' difference (Taussig alterity/mimesis and Greaber). How then can biases then be revised by decolonial approaches? For example, by animism, and through indigenous languages, meaning and context? Would it be possible to build a decolonial AI? Decolonial thinking, from an African perspective, challenges the dualism inherent in scientific thought through a relational, contextual, and historical approaches (Fanon, 1952). A decolonial approach could play a crucial role in identifying and addressing biases in machine learning by examining the historical and contextual factors that shape data agencies. This approach could enable a more nuanced understanding of the information being processed, thereby enhancing the accuracy and cultural sensitivity of the outcomes. Deep learning mechanisms that prioritise calculation in data sets and corpus-gen-AI, what are the issues where can a decolonial position materialise? Possible entry point: Assessment of base code at the test and evaluation stage may be helpful, considering the potential impact on model performance and efficiency from a decolonial perspective. Additionally, incorporating feedback loops during the training process could further optimise the deep learning mechanisms for improved results and how use cases/biases in data sets could impact protective classes and be revised before impact by the machine. Ethnography and Large Language Models Inclusiveness: African philosophy and artificial intelligence - value (mis) alignment. Development theories prioritise control over the environment, individual freedom, self-interest, and market dynamics. These concepts contribute to welfare, private property, materialism, and the unification of value through the instrumentalization of the market and the production process. All these factors play a role in the labour force process and changes to market dynamics through the development of new technologies. Ultimately, these concepts play a critical role in shaping society's economic structure by influencing various aspects of the economy and technological development. New digital divisions related to technological advancements are emerging and issues such as capacity building, new labour redundancies, privacy concerns, and ethical challenges will continue to develop. Unlike previous waves of automation, a new wave of automation will impact a significant portion of the economy, particularly middle-class occupations. There will also be new opportunities for employment, new job positions, and increased productivity and previously undervaued parts of the economic activity such as caring and social service occupations will become increasingly valuable. Is it possible to develop intercultural digital/data ethics? Deep learning principles that incorporate ontologies and indigenous languages, as well as diverse perspectives, could help address some of the ethical challenges in the digital age. Collaborative efforts between different cultures and communities will be essential to ensure that digital advancements are developed and implemented in a responsible and inclusive manner. African Philosophy The Nguni languages of southern Africa are the foundation for the concept of reciprocity, also known as Ubuntu (I am because you are) which enhances concepts such as the economy, inclusivity, and reciprocity, refining their understanding. It considers how humans and non-human beings (nature and beliefs) are more important than economics. All living things, including nature, and the earth are closely interconnected and interdependent, emphasizing unity over separation. It encompasses the idea that each individual contributes to collective well-being, fostering harmony within society and 'intelligence' may also be generated through relational beings and as a whole (collective intelligence). The grammar of African languages e.g Muntu and Bantu (which focus on personhood) enables exploring Ubuntu-related ideas embodying the fundamentals of African thought. While English is a noun-based language with teleological future thought, African languages (with complex noun class systems) prioritise motion and verbs, emphasising the present. For example, people who came before you are part of the living community, and future generations also shape the community that exists today. The decisions you make are influenced by those who preceded you and those who will come after you ( links to critical race theory and historical perspectives ----expand with taussig and defacement and labour of the negative analysis https://www.sup.org/books/title/?id=432). This is in misalignment with transhumanism because transhumanism can be based on a restricted worldview; the concept of robots replacing humans and developing general intelligence presupposes the ability to separate and individualise intelligence, which is in contention with the concept of Ubuntu (collective intelligence). Where are African ethics in AI located? AI and language. Language is used in programming in AI and could be for a shared intercultural digital ethics. Including African languages in computer programs, connecting different dialects, embracing their African perspective, and moving away from English as the standard could be considered. How will bias continue to impact AI and Data? Are there interventions points to reduce bias? Or will they decrease or exacerbate bias? Will it make AI more transparent or more opaque? What does economics entail? Governance and the bottom-up approach? Ontologies and deep learning-- How can a framework be constructed from an intercultural viewpoint? How could a decolonial approach account for data agencies and bias in data be developed as a critical approach, and ultimately how to get beyond that? Can we use “polyvocality,” a movement towards the inclusion of multiple perspectives in and on data agencies? How reliable is the data to obtain the perspectives of different stakeholders and the relationships between them and the data agencies? What theoretical framework do we use to define perspectives and their relations with the underlying data? Understanding polyvocality in machine learning will require a methodology that can accurately capture the diverse perspectives of stakeholders. A combination of qualitative and quantitative research methods may be beneficial in developing a theoretical framework that defines perspectives and their interconnectedness within machine learning and data agencies.[[File:Data_agencies_image|thumb]] Examples? What could some alternatives look like? Especially those that account for people? Challenges to data agency approach. Political Economy approach to data agencies: rather than abandon the categories of “subject” and “object” and of “Society” and “Nature,” as suggested by proponents of “the ontological turn,” researchers can compare subject–object transformations and the naturalization of social power relations in the two contexts. In acknowledging the ultimate dependence of modern technology on exchange rates and financial strategies in a globalized economy, we realize that the agency of modern artifacts is also dependent on human subjectivity. In shifting the focus of comparative anthropology from ontology to political economy, we can detect that modern technology is a globalized form of magic (Hornborg, A, 2015: 35). How do Data agencies relate to Roy Bhaskar's Critical Realism and the Philosophy of Meta-Reality Part II: Agency, Perfectibility, Novelty. Would a multi methods approach to data agencies be an appropriate method? Can a data toolkit be developed to analyse data agencies during research for arts and humanities researchers? Data Empowerment for Empowerment of Arts and Humanities Research. Interdisciplinary work centred on data activism and justice research is key, as well as how to avoid mistranslation and develop a common language for arts and humanities researchers to use. Add decolonial data methods and data agencies toolkit Examples: Part of this will include how to feed into technical and legal frameworks to raise awareness and question the coding and design of data sets. But also how researchers might be empowered by legal, policy, and technological advancements, as well as what else needs to change. Data empowerment could also entail legitimising a non-scientific worldview of data through the arts and humanities' multiple complexities and values.

Inclusive Data Research Skills for Arts and Humanities/Deconstructing data methods and decolonising approaches. Defining the challenge, mapping out contexts and key stakeholders. When defining challenges, it is necessary to deconstruct and unpick the kinds of thinking, epistemologies and colonial practices that come with data tools, skills and methods. Embracing the collaborative and plural nature of a hackathon, we contextualised the relationship between coloniality and data methods through multiple perspectives and disciplines. We began to map out who the key stakeholders are, what are the core systems, institutions, frameworks we need to address. 1. Defining our key terms:. data data methods data epistemologies coloniality decoloniality 2. Reflections:. How do we encounter coloniality or de-coloniality from our lived experiences and/or within our particular disciplines? Where do data oriented computer tools or data practices come into this? What is the data being collected, who or what is being observed and who is observing, how is the data being labelled or categorised, what is the data used for? How are data methods embedded within colonial disciplinary priorities and biases? "For instance: categorization, thinking in binaries, quantification, reduction, cultural privileging of “rational” science, the myth of ‘objective data’, the capitalisation of data, extractive data practices, bias, environmental extraction required for data processes to run." Some of the areas discussed include: How do the limitations of data or computational tools force certain worldviews or certain ways of thinking? Include examples of systems participants use within their field.. "E.g. AI, search engine systems or research databases, social media analysis tools, facial recognition systems, surveys and questionnaires offering limited identity categories" DATA GAPS / ABSENCE. "E.g. research gaps, theoretical gaps, access to computation and data. Do we need to fill gaps or how can they be useful? HI-RES/LO-RES BIAS certain geographical locations have concentrated data and concentrated technologies" Some points of discussion included: Alternative Lens, mapping out key approaches and movements. What does a decolonial lens look like when working with data? What can we learn through examining the broad approaches different groups are taking in order to counter hegemonic powers? Themes and approaches include: Counter-Investigation. Groups using data methods to hold hegemonic powers to account, whilst also reflecting and making aware the biases embedded in such tools. Abolitionist perspectives on technology:. When data / technologies prop up military and policing, it is argued these practices can never be decolonial, so abolitionist principles may be a framework to think through these. Co-created resources:. "The following pointers are suggested principles and reflective questions for researchers/students to approach decoloniality when working with data methods." "The task of decoloniality is always changing, this is not a definitive set of answers." Key values that need to be central when approaching challenges: Create a loose set of guidelines/principles for arts and humanities projects working with data methods for how to challenge coloniality. What are some critical questions to prompt reflection on projects in relation to decoloniality? What practical and technical tools do we need? What perspectives, thinking tools, frameworks, theories do we need to engage in decoloniality?

Inclusive Data Research Skills for Arts and Humanities/What counts as data?. In an arts and humanities context, what counts as data can be any observable traces of interactions and occurrences. In arts and humanities research, any of a vast and complex set of recordable observations and artifacts fit into some definition of “data”, and the contextual issues around their interpretation depend on the form of data that is being dealt with. The following page is an attempt to provide broad categorisations and distinctions between different kinds of data, and address some of the challenges in their use. The absence of data also constitutes data (was something destroyed or lost, is something disregarded or in a foreign language or physically/digitally inaccessible, etc.). Potential Categorisations. Data categorisations are influenced by a variety of factors including research objectives, research questions, research design, existing standards within a specific discipline, approaches to information interpretation, nature of sources of information, and psychological connotations, among others. How researchers collect data also plays an important role in how this data is then perceived, categorised and analysed. Qualitative and quantitative data. Researchers’ objectives determine the methods used and approach to data. Depending on the aims of the research and research questions, data could fit into qualitative and quantitative categories. For example, surveys can provide both qualitative and quantitative data depending: what % of people answered yes or no to certain questions? OR describe, interpret and contextualise each response. Analogue and digital data. The nature of the sources of data determines if we can classify data as analogue (physical) or digital. Analogue Data - Trace of interaction with the surrounding world through time. Research objectives and questions also influence the classification of data as analogue or digital. For instance, a book historian analysing a digitised version of a manuscript would consider it to be a source of analogue/physical data, while a researcher analysing the level of digitisation of a collection would most likely consider it to be a digital source.

Inclusive Data Research Skills for Arts and Humanities/Data Visualization. https://docs.google.com/document/d/1YtF1zgLYFd_YpS2AVXE7UxbWaYS3BJpYTSU3GPY8_PY/edit Definition(s). "Data is not just numbers but information (Gitelman, 2013)" What is data visualisation? Data visualisation is the process of interpreting information through distinctive and contextually relevant concepts and translating the data into a visual narrative. It connects readers to insights, knowledge and power, and it can generate new relevant questions. Medium to Why is it valuable for A&H researchers? Data. Data gathered in the course of qualitative research can vary dramatically depending on the subject of investigation. For example, data can be gathered through discussions, interviews, observations, photographs, painting, video and audio. This can pose both a challenge and an exciting creative opportunity for the qualitative researcher. It is also important to remember that the interpretation of qualitative data can be subjective. It is therefore important to consider your own positionality and bias when embarking on data visualisations. Data visualisation in research. Data Visualisation is used in multiple ways within arts & humanities research, for numerous purposes.  This can include visualisation of both qualitative and quantitative data, however within arts and humanities particular consideration is needed regarding visualisation of qualitative data.  Whilst qualitative data can be translated into categorical or numerical data, there is a balance to be struck between simplifying the richness of a qualitative dataset and capturing its complexity for the purposes of visualisation.  Within arts & humanities data visualisation is used for distinct purposes beyond graphical output of research outcomes and can inform processes of discovery and the research journey. These purposes should not be seen as distinct, as they interact with each other, and also interact with other phases of the research journey including research outcomes. Purposes of data visualisation include: Data visualisation as an outcome. Data visualisation can be used to tell a story narrative; presenting information in a way which is accessible to the wider public. This is particularly important when exploring nuanced or complex questions or topics which a layperson may be unfamiliar with. Benefits of data visualisation: When visualising your data, it is important to consider what your priorities are. If the goal is to make information as accessible as possible, this may affect how and where you present your data. Creating your own website may be a compelling idea but this requires continuous funding/resources. Will a website of your own design still be accessible in five years time? When possible, data can be made open to improve accessibility. You may want to consider sharing data you have collected on sites like . Visualising data can also be beneficial for the researcher by providing them with the opportunity to reflect on their research. Gaps in data can be identified and new questions can be raised. Improving data visualisation. Visualisation of data for un/underrepresented groups when the baseline for that data collection is ‘0’ might start from below the lowest data point to allow the visualisation of a lack of data. Missing data can be visualised by a showing of the lack of data, generally demonstrated through spatial or visual gaps and juxtapositions within data visualisations . Qualitative data is often visualised through methods of quantifying qualitative information. We should find new ways of visualising or interacting with data through qualitative means. Chord, network, and Venn diagram charts may be a few ways of doing this effectively, but what are other innovative ways that we may view qualitative data? How might other modes of engagement be used to understand this data (i.e. sound, touch). Something here is needed about using multiple visualisations to engage in varied interpretations. Tools we're already using. Tools used to collect, code, analyse, explore, and share:

Inclusive Data Research Skills for Arts and Humanities/Data practices. Object of study > Data Problem(s)

Philippine History/Prehistory. Homo luzonensis, also locally called "Ubag" after a mythical caveman, is an extinct, possibly pygmy, species of archaic human from the Late Pleistocene of Luzon, the Philippines. Their remains, teeth and phalanges, are known only from Callao Cave in the northern part of the island dating to before 50,000 years ago. They were initially identified as belonging to modern humans in 2010, but in 2019, after the discovery of more specimens, they were placed into a new species based on the presence of a wide range of traits similar to modern humans as well as to Australopithecus and early Homo. In 2023, a study revealed that the fossilized remains of the Callao Man has been found out to be 134,000± 14 years old and much older than previously known.

Inclusive Data Research Skills for Arts and Humanities/What kinds of research can data-oriented arts and humanities researchers do and what are the possible challenges?. Data-oriented arts and humanities researchers can:. Digital and data skills are becoming increasingly important parts of the arts and humanities. These kinds of data that have always played an essential role in Arts and Humanities are available in digital formats. Equally, the lives of everyday people are now played out in ways that develop digital data, creating new sites and opportunities for arts and humanities research. In terms of arts and practice research, data can play a crucial role in many forms of digital art, making digital and data skills a valuable tool for creation of new art and insights. Using digital and data skills, Arts and Humanities researchers can: Arts and humanities researchers can also use their knowledge of data and digital skills to question those skills using the knowledge of data as a way of pushing back against assumptions and biases of technology and technology-based research. Digital and data skills can be tools of resistance. Some of the challenges are:. While digital and data skills can provide many opportunities for arts and humanities researchers, there are also some challenges. Over the course of the DAReS project we have identified some of these challenges, and where possible started to look at ways of addressing some of these challenges. While there are specific challenges, one of the difficulties can be with regard to culture and language. This is a twofold challenge; on the one hand, those from a science, technology or data background may use specific disciplinary language and have an expectation of background using computers, maths and programming. While this background isn’t needed, for example, learn to code in Python. On the other hand, language and culture can be challenged when working with data, as most data is collected from a Western colonial perspective and may only sometimes inherently create space for alternative viewpoints. As arts and humanities researchers, we can bring our knowledge and expertise of critical perspectives to data research as a way of questioning and interrogating the data – this is done with more authority when speaking from a place of having sufficient knowledge of the skills and tools. Other challenges can be: Some of the core ways of overcoming these challenges include:

Inclusive Data Research Skills for Arts and Humanities/Who can potential collaborators, partners and team be?. Collaborators and Partners- getting the team together. Partners, collaborators and team members for data research projects in the arts and humanities could come from many places. They could be: Like any other research project you want to bring together the group of people who are best suited to the goals and process of the project. Data research projects in the arts and humanities often inherently involve a certain amount of working across disciplines and methodological approaches. This is something to be embraced. In addition, look at the funders and their funding priorities and then make connections with potential academic and other partners that can boost your credibility and endorse your creative practice. Especially for those at the start of the research journey. Contacting Collaborators of and Project Partners. Projects often emerge organically, and in those cases, you may already have partners and collaborators in mind. A research project can be a great way to develop a research relationship, build new skills through collaboration or explore a new disciplinary area of research. One of the best first steps in building your research team is to develop a single-page summary of your project. This should be seen as a working document that can develop over time. It is important that when you are bringing new people on board, you do not overly ‘nail down’ the research project so that the project can benefit from the wide range of expertise in development as well as execution. Collaborators. Collaborators will include the people who make up the research team, these may be Co-investigators, consultants, researchers or have other titles depending on the funder. Working on research in collaboration can be very rewarding and lead to new findings and outcomes that might not be possible working on your own. Collaboration can be a great way to benefit from senior researchers’ experiences, and it can be an opportunity to mentor junior colleagues, who may bring new energy, approaches and enthusiasm to a project. When building a research team a few things to consider are:. 1)        Ensure diversity of experience and backgrounds – this is important for inclusion but also avoids groupthink. 2)        Start with contact you may have a relationship with first, or ask for an introduction through your networks, emails out of the blue are often missed. 3)        Think about any skill gaps your team may have, and consider who you would like to fill those gaps. 4)        Work across institutions and geographic areas as this can significantly broaden the scope of your project. If you are applying for UKRI fuinding, once you have built your team you will need to compete a R4RI which sets out why your team has the skills you need for the project. Project Partners. Project partners refer to organisations with a stake in the project but not part of the core delivery team. For UKRI grants, specifically, project partners must materially support the project (through support in kind) but cannot be directly paid for by the project. You can generally provide things such as travel, accommodation and subsistence if you need them to attend meetings, workshops or similar.   As part of your grant application process, you will need to ask your project partners to write a ‘letter of support’ which states why the project is relevant to their organisation, why they think it is worth supporting, and what material support/support in kind they will be providing. Project partners can help you shape your project, ensure there is a clear pathway to impact and give you new access to key communities relevant to your project. It can be very useful to contact partners early in your process as they may have insight into how to make the project stronger, including making it more relevant to their work and more more likely to have an impact. When planning your project make time to meet with potential partners as they may have questions or input, and while not every potential partnership pans out for the specific project, these initial conversation can be essential to future collaboration.