Becoming-Infrastructure: Datafication, Deactivation and the Social Credit System
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Notice bibliographique
Résumé
How might critical library and information studies analyze the intersection of information infrastructure and class structure? The emergence of big data through "datafication" rests on the historical process of information and communication technology (ICT) production and distribution. This paper explores the concept of datafication as an integrated component of information infrastructures unfolding within the class structures of capitalism. A critical realist perspective on relational sociology is offered to illustrate how heterogenous data sources are combined and configured to activate materials and bodies into new internal economic class relations of control. My analysis of datafication therefore moves beyond isolated conceptions of "information" and toward the capacity of distributed data sources to extend and deepen class structures. Two recent large scale cases of datafication are analyzed to highlight its causal powers within class structured society. The first case is drawn from a New York Times article concerning the subprime automobile loan market in the United States. The article details the installation of surveillance technologies into the vehicles of people segmented by low credit scores as a condition of exchange for subprime loans. As a result of this exchange, surveillance technologies capture borrower's driving behaviors and locations in real-time data flows. These data flows are analyzed according to interest bearing payment regimes, rendering both vehicle and borrower as manageable assets while conferring onto lenders the power of remote automobile deactivation. This suggests datafication of driving behaviour produces new implications for class conditions when such data are integrated with the structures of the subprime market. The second case detailed in several news articles examines the plan for a large scale top-down cybernetic behavioural programming initiative by the Chinese government termed the "social credit system," built from digital traces of multiple economic and non-economic social behaviours of its citizens. While aspects of this system are currently voluntary, they are expected to become mandatory within five years. Ubiquitous surveillance of digital activity never before combined into a predictive and prescriptive score may be considered a nation-wide disciplinary subsumption of social activity under novel valuation algorithms, integrating previously unwatched or irrelevant external activities into new internal relations determinative of class structured possibilities. The plan for a social credit system appears driven toward developing a seamlessly interconnected national behavioural identity for every Chinese citizen, which may produce structural implications for pre-existing class conditions. I suggest these cases are examples of the need for library and information studies to engage critically with the emerging causal powers of information infrastructures theorized here as deepening capitalism's control of class structures.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,000 | 0,012 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle