Information & Meaning in the Social Sciences: Enclosure, Capital, Metaphor & Method
Bibliographic record
Abstract
This doctoral dissertation deals with the question of "information" in the social sciences at various levels: (1) Enclosure, (2) capital, (3) metaphor, and (4) method. Over the past decade, machine learning approaches relying on information metrics and using textual data have become commonplace in all of the social sciences and humanities. Behind this turn, we find a peculiar paradox: Information, a concept which originally was strictly about engineering, is today the arbiter of "meaning", semantics, and even place. In chapters one, two, and three, I explore this paradox through a set of genealogical studies on the various moments of translation that had to be established for information to become the (1) enclosure within which meaning can be disclosed. The relationship between information and meaning has gone through a fundamental shift, where, initially, it was meaning that appeared as the agreement which established the conditions for information. As the roles have reversed, the large language model (LLM) has become an avenue for accumulating and producing a new type of (2) "cosine capital" which determines which truths can be disclosed, and which cannot. In this sense, information today also acts as a (3) metaphor, though instead of being recognized as such it is habitually naturalized. Chapters four and five take a playful approach to this state-of-affairs: How could information theory help us re-imagine the information asymmetry in a particular context, namely between tenants and landlords? In chapter five, we take to task extant work in critical housing and algorithm studies, challenging prevailing theories on opacity and information asymmetry. As an alternative, we develop a new concept of information asymmetry in the housing system and demonstrate its utility in a comprehensive study of landlord networks in the Montreal rental market. Chapter six functions as a sort of postscript to the previous chapter, developing an intuitive mathematical approach to information asymmetry in a given landlord network using the Wasserstein Distance metric. The chapter also acts as a segue to the rest of the thesis: It moves from information as metaphor to information as quantitative (4) method, closing the circle from the beginning of the dissertation through two overlapping studies of Airbnb descriptions and reviews in New York City. In these two chapters, I take prevalent operationalizations of information theory as granted, focusing instead on developing a new framework for quantitative analysis in critical toponymy studies. Through a new model for named entity recognition (NER) of spatial language, I show how existing census based metrics of gentrification can be complemented by analysing the geographic "span" of neighbourhood names along with strategies such as claims about proximity to prominent attractions and areas
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".