Information & Meaning in the Social Sciences: Enclosure, Capital, Metaphor & Method
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,027 |
| Communication savante | 0,009 | 0,015 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».