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`x-dexing` is a cross-reading practice that through chance operations guides the reader in going over a body of texts. Contrary to an index, the `x-dex` invites to perform a non-linear distribution of attention across textures, semantics and aesthetics of the texts at hand.
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While `x-dexing`, we read a collection of texts (materials) from a particular perspective (handles) across forms (text, color, curves, absences). To start with `x-dexing`, you are prompted to choose one *piece of material*, *handle* and *form*. In the next step, you are invited to write a *score*: a short set of rules for a particular way of reading. As a last step, you execute your own score, visualing the ruleset in a *trace*. `X-dexing` can happen in a concentrated manner, engaging with materials, given scores or invented ones, using these forms or others. But it can also be operated slowly, along time, in an ongoing way.
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If “indexing” would be about gaining access through the illusion of completeness, the `x-dex` is about situated unfoldings, about letting go of fixitude and about handing over for a little longer; a form of generative relationality that is not providing with control nor indication, but a sort of playfulness and imaginative re-entanglement. Perspectives, feelings, aesthetics or uneasiness are not only brought to the table by the agents that share materials, but by the emergent `x-dexer` as well. Together they contribute to an explicit toolset for handling difference patterns, operate with worldly absences, and score open questions.
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`x-dexing` was made by Jara Rocha and Manetta Berends to navigate and cross the book Iterations (2020), and appeared in a couple of other workshops and different versions since.
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`word2complex` is taking the algorithm word2vec as starting point for a discussion around how users can read alongside algorithms and how algorithms construct machinic readings of text. As a reading exercise, `word2complex` investigates how algorithms order our experience and understanding of relationality between different words, by calculating semantic distances and context similarities. By staying close to the logics of word2vec, `word2complex` aims to rethink contextual ways of calculating and proposes to form semantic distances between contexts in more-than-computational ways.
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Word2vec derives meaning not from semantics but from structure, relation and repetition, which it then turns to vectors in a multi-dimensional space. In `word2complex`, participants respond to such a way of processing text through a form of slow processing. In the first step, they count the amount of times a word appears in the text. In the second step, they rebuild the context of the word in the different sentences they encouter where the word is being used and in the third step, they relate the words with each other, resulting in text patterns.
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Language analysis algorithms are pervasive in an online realm that is heavily text-based. Particularly word2vec is often used to cluster, make suggestions of related items, to translate to other languages, or to bring up results in search engines. By following the particular logic of the algorithm but leaving space for interpretation and co-production of meaning with those present, `word2complex` attempts to keep the complexity in operations of language processing through situated readings.
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`word2complex` is a continuation by Manetta Berends and Cristina Cochior (2021) on the collective unfolding of word2vec in Algolit (2017).
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