Pablo Velasco
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--- title: ITRMdm2 tags: class slideOptions: theme: white transition: slide --- ## <span class="censor">ITRM 2021 // Week 8 - Digital Methods 2</span> <!--image for class--> <img src="https://i.imgur.com/ieLjqFy.png" width=50%> <span class="refs">Rieder, B. (2020). Chapter 7: From Frequencies to Vectors. In Engines of Order. Amsterdam University Press.</span> Pablo Velasco // Information Studies // [pablov.me](https://pablov.me) --- ## A SOCIOLOGY OF QUANTIFICATION <span class="refs">Espeland, W. N., & Stevens, M. L. (2008). A Sociology of Quantification</span> **Numbers that** * mark (create categories, e.g. Dewey system) * <span style="color:hotpink">commensurate</span> (attempt a common metric) * nominal * ordinal * <span style="color:hotpink">interval</span> (ratio) <a href="https://corporatefinanceinstitute.com/resources/knowledge/other/carbon-credit/"><img src="https://cdn.corporatefinanceinstitute.com/assets/carbon-credit.jpeg" width="30%"></a> <span class="censor">"it transforms all difference into quantity"</span> ---- ## Five sociological dimensions of quantification 1. **Work** (e.g. https://dkuni.dk/uncategorized-da/universiteter-klar-med-model-for-flere-studerende-til-mindre-byer/) <span class="censor">Rendering France statistical required a transformed nation as well as statistical innovation</span> 2. **Reactivity** (e.g. [ghettolisten](https://www.trm.dk/publikationer/2020/liste-over-ghettoomraader-pr-1-december-2020/)) <span class="censor">The aim of statistical work is to make a priori separate things hold together, thus, lending reality and consistency to larger, more complex objects (Desrosières 1998)</span> 3. **Discipline** (e.g. GDP and [global economy](https://books.google.com/ngrams/graph?content=the+economy%2C+economy&year_start=1800&year_end=2019&corpus=26&smoothing=3&direct_url=t1%3B%2Cthe%20economy%3B%2Cc0%3B.t1%3B%2Ceconomy%3B%2Cc0)) <span class="censor">Seeing something is the first step to controlling it</span> 5. **Aesthetics** <span class="censor">The most successful numerical pictures influence the ontology of what they represent. The picture becomes its own subject, replacing, in the comprehension of the observers, what it originally was intended merely to depict</span> ---- 5. **Authority** * <span style="color:hotpink">statistical realism</span> (independece between object measured and act of measuring) * *metrological realism*: statistical observation embeds reality to objects * *accounting realism*: reality of objects measured by financial accountablity (objectification of value) * *proof in use*: "raw" data as self-sufficient proof (policy oriented) * <span style="color:hotpink">statistical constructionism</span> (statistics as elements for production of reality) * <span class="censor">How constructed and object or relation appears is a function on how successful groups have been in securing its durability and legitimacy, in making it seem inevitable</span> <span class="censor">"We cannot understand the basics terms of justice if we don't understand quantification"</span> <span class="refs">Espeland, W. N., & Stevens, M. L. (2008). A Sociology of Quantification</span> --- <!-- .slide: data-background-image="https://i.imgur.com/UZsF8iT.jpg" data-background-size="100% auto" --> ---- ## FREQUENCIES TO VECTORS / STATISTICS Problem: which words to use to use as *index terms*? (Peter Luhn) 1. Solution: use of *stopwords* (<span style="color:hotpink">manual curation</span>) 2. Solution: more frequent words are less 'meaningful', and the less frequent, more informative (<span style="color:hotpink">statistical</span>) <span class="censor">The first type relies on specifically human faculties while the second is the work not merely of a machine, but of a system that implements a particular technique or method to make the material 'speak'(206)</span> <span class="refs">Rieder, B. (2020). Chapter 7: From Frequencies to Vectors. In Engines of Order. Amsterdam University Press.</span> ---- ## AUTOMATION / COGNITION Co-word analysis <span class="refs">Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis.</span> <span class="censor">The computer produces an appreciation of the texts it parses that is not a simulation of human understanding but a different breed of cognition that enables different ordering practices (212)</span> ## "Proof is replace by benchmak" <span class="refs">Rieder, B. (2020). Chapter 7: From Frequencies to Vectors. In Engines of Order. Amsterdam University Press.</span> --- # <span style="color:hotpink">ACTIVITY</span> 1. Use your own text (you'll need a *.txt file) to identify sentences, entities and parts of speech 2. How can a (statistical) text analysis be integrated to your planned research? 3. Which of the sociological dimensions of quantification is relevant to discuss in your planned research and how? <style> .reveal{ font-family:arial; font-size: 26px; } .reveal .censor{ background:black; color:white; } .reveal .censorw{ background:white; color:black; } .reveal .pinky{ color:#e5157d; font-style:italic; font-size: .8em; } .reveal section img { border: none; box-shadow: none; } .reveal section left{ width:50%; } .reveal .refs { color: grey; font-size: small; text-align: left; } </style>

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