113 lines
4.0 KiB
HTML
113 lines
4.0 KiB
HTML
{% extends "en/base.html" %}
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{% block title %}{{query}}{% endblock %}
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{% block nav %}
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<div id="print" class="blink {{ connection }}">
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<a href="?q={{ query }}&print=now&results_count={{ results_count }}"><img src="/static/images/epson.png"></a>
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</div>
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<script>
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(function () {
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var printing = false,
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button = document.querySelector('#print a');
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button.addEventListener('click', function (e) {
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if (!printing) {
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printing = true;
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button.classList.add('printing');
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}
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else {
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e.preventDefault();
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alert('Already printing ...');
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}
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})
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})();
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</script>
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{% endblock %}
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{% block results %}
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{% if results == {} %}
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<div id="notused">This word could not be found.</div>
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{% else %}
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<div class="cross">
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<p class="note">[Note on the search results]</p>
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<p class="tfidf">Once a search query is submitted, the <em>TF-IDF</em> starts to go through all the manifestos in the dataset, to see if a word is used, and if yes, to put it in the list of search results. The list is sorted according to the importance of the word <strong>{{ query }} </strong> to each manifesto. </p>
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<p class="techfem">The search results are snippets, statements, quotes or questions, threaded together by one single word, create a cross-reading index. The occurances of <strong> {{ query }}</strong> in the different manifestos, become eachother complexifiers. </p>
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<br>
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<br>
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</div>
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<div id="results">
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<h1>Cross-reading through the manifestos along the axes of <strong class="query">{{ query }}</strong>:</h1>
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<div class="results">
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{% for x, manifesto in results.items() %}
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{% for sentence in manifesto.html %}
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<div id="{{ x }}_{{ loop.index }}" class="result">
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<div class="title">
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{{ manifesto.name }}
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</div>
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<div class="sentence">{{ sentence }}</div>
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</div>
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{% endfor %}
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{% endfor %}
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</div>
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</div>
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{% endif %}
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{% endblock %}
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{% block suggestions %}
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<div id="suggestions">
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<h1>SUGGESTIONS</h1>
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{% if analytics %}
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{% if analytics.stemmer %}
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<div id="similars" class="analytics stemmer">
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<h2>Similar but not the same to <em>{{ query }}</em>:</h2>
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{% for word in analytics.stemmer %}
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<strong class="word"><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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{% endif%}
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{% endif%}
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<div class="suggestions techfem">
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<h1>Cross-reading suggestions (manual selection):</h1>
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{% for word in suggestions %}
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<strong class="query"><a href="?q={{word}}">{{ word.strip() }}</a></strong>
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{% endfor %}
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</div>
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{% if analytics.mappings %}
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<br>
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<div class="suggestions tfidf">
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<h1>Cross-reading suggestions (algorithmic selection) through <em>{{ results[0]['name']}}</em>: <br></h1>
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<div class="">
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{% for tfidf, word in analytics.mappings[:50] %}
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{% if word == query %}
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<strong class="word">{{ word }}</strong>
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{% else %}
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<strong class="word"><a href="/{{ lang}}/?q={{ word }}">{{ word }}</a></strong>
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{% endif%}
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{% endfor%}
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</div>
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<br>
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<div><a class="contrast" href="/{{ lang}}/mappings/{{ results[0]['filename'].strip() | replace('.txt','') }}">Read more ◐</a></div>
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</div>
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{% endif%}
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</div>
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<div class="cross">
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<p class="note">[Note on activating]</p>
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<p class="tfidf">
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The <em>TF-IDF algorithm</em> is an activator. Activating non-linear threads through a dataset of words.
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<br><br>
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As a navigator the algorithm is a provider of order, deciding on the importance of the search term for a manifesto.
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</p>
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<p class="techfem">
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The <em>cyber- and technofeminist manifesto</em> are written and published to activate. Declaring intentions, motives or specific views on technology. Their writing styles, different as they are, are often statement-based, short and sometimes militant.
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<br><br>
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<em>"Nothing should be accepted as fixed, permanent, or 'given' -- neither material conditions nor social forms.."</em> (Xenofeminist manifesto), <br>
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<em>"So that is why no revolution should be without her."</em> (The Manifesto of Futurist Woman)
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</p>
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</div>
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{% endblock %}
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