53 lines
4.3 KiB
HTML
53 lines
4.3 KiB
HTML
{% extends "en/base.html" %}
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{% block results %}
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<div id="intro" class="cross">
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<!-- <p>Psst, this is a cross-reading tool that operates on two axes ...</p> -->
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<!-- <p>Tip: Try to search for a single word.</p> -->
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<p>A cross-reading allows for a specific type of reading that does not follow a linear order, but follows a self-chosen path. Reading becomes an act of creating relations and threading connections, reading across different domains. </p>
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<p>This cyber/technofeminist cross-reader does not follow one but two axes, bridging the act of reading a collection of texts, with the act of reading a tool. Reading across a collection of manifestos, while reading the algorithm that is used to do so.</p>
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<p>These cross-readings connect ...</p>
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<p class="tfidf">... the <em>Term Frequency Inverse Document Frequency</em> algorithm, or <em>TF-IDF</em> in short</p>
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<p class="techfem">... a collection of <em>cyber- and technofeminist manifestos</em></p>
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<p class="tfidf">The TF-IDF is a commonly used algorithm to find the most important words of a document. The algorithm is (partly) written by the female computer scientist Karen Spärck Jones in the 1970s and has become one of the important algorithms of many search tools online, such as digital library systems or corporate search engines like Yandex or Google. The algorithm turns written documents into a sorted lists of search results, using a specific relative and inversed way of counting, that is sensitive for contrast in written documents. </p>
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<p class="techfem">The cyber/technofeminist manifestos connect feminist thinking to technology, introducing feminist servers, cyborg figures, cyberwitches, or pleas for the glitch as cultural digital artefact. This collection, which is obviously incomplete, brings a diverse set of technofeminist documents together that are published between 1912 and 2019. The manifestos speak about very different concerns and questions, but they connect in terms of energy level. Urging to make a statement, ready to activate.
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<br><br>
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An interesting note to mention: Karen Spärck Jones was an advocate for the position of women in computing. <em>“I’ve been trying to think a little bit—but it’s very dispiriting!—about how to try to get more women into computer science. On the whole, everybody who thinks about this is depressed, because we’re going backwards rather than forwards.”</em> <sup><a href="https://ethw.org/Oral-History:Karen_Sp%C3%A4rck_Jones#On_Getting_More_Women_into_Computer_Science">•</a></sup></p>
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<p>These two axes, the algorithm and the manifestos, interoperate. They support and strengthen eachother as the X and Y of this cross-reading tool. </p>
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<p>The TF-IDF algorithm, while responding to a search request, creates cross-readings through the manifestos. It outputs a list of search results around the subject of search, creating a field of statements, questions and concerns around one single word. Meanwhile, the algorithm starts to interoperate with the manifesto as a format. Sensitive as it is for bulletpointed writing, repetition and unique words -- elements that are used a lot in these statement driven documents. The algorithm prioritizes higher contrastful language over academic writing, repetition over very diverse vocabularies and the use of unique words over the use of common ones.</p>
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<p>See this cross-reading tool as an exercise in reading, across a field of technofeminist thinking and a tool for algorithmic sorting.</p>
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<!-- <p>Reading the TF-IDF algorithm by itself only results in a technical understanding of this old artefact.</p> -->
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<!-- <p>This algorithm thereby connects a discrete procedure (that of computing) to a very vivid and strong document format: the manifesto.</p> -->
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</div>
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{% endblock %}
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{% block suggestions %}
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<br>
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<h1>Cross-reading suggestions (selected):</h1>
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<div>
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{% for word in suggestions %}
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<strong class="query"><a href="/{{ lang}}/?q={{word}}">{{ word.strip() }}</a></strong>
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{% endfor %}
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</div>
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<br>
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<h1>Contrast-mapping suggestions (top 100 TF-IDF results):</h1>
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<div>
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{% for tfidf, word in mappings %}
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<strong class="query" style="font-size:{{ 50 + tfidf / 5 }}%;"> <a href="/{{ lang }}/?q={{ word }}">{{ word }}</a> </strong>
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{% endfor %}
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</div>
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<br>
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<p>Read more about the <a href="/{{ lang}}/mappings/{{ filenames[0] }}">TF-IDF algorithm and contrast mappings</a>.</p>
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<br>
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<br>
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{% endblock %}
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