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Big Data, gouvernementalité et industrialisation des médiations symboliques et politico-institutionnelles

2018· article· fr· W2901263839 on OpenAlex
Marc Ménard, André Mondoux, Maxime Ouellet, Maude Bonenfant

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterfaces numériques · 2018
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Dans cet article, nous interrogeons l’intégration des médias socionumériques au sein de dynamiques globales de production, de circulation, de captation et de traitement de données (Big Data). Plus spécifiquement, nous analysons comment ces dynamiques contribuent au déploiement d’un nouveau mode d’objectivation social. Il s’agit, d’une part, de montrer comment ce mode d’objectivation repose sur l’intégration des médias socionumériques au sein de circuits marchands. D’autre part, nous éclairons comment le processus d’industrialisation du traitement des données personnelles induit de nouvelles modalités de régulation sociale fondée sur des procédés algorithmiques. Nous présentons enfin comment se traduit cette « gouvernementalité algorithmique » sur le plan des représentations symboliques constitutives de l’« objectivité » que prennent les rapports sociaux et de la signification que les sujets accordent à leurs pratiques.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0050.012
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.610
GPT teacher head0.417
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it