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Record W4214689159 · doi:10.4000/ethiquepublique.6538

Du biopouvoir à la Gorgone

2021· article· fr· W4214689159 on OpenAlexvenueno aff
Caroline Lequesne Roth

Bibliographic record

VenueÉthique Publique · 2021
Typearticle
Languagefr
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophyArt

Abstract

fetched live from OpenAlex

À l’heure de la numérisation de nos existences, la multiplication des systèmes algorithmiques de surveillance accélère ce que d’aucuns identifient, après Michel Foucault, comme l’expression paroxystique d’un « biopouvoir ». Cet avènement résulte de l’histoire elle-même du politique, intrinsèquement liée à celle des sciences et des techniques que la dernière décennie a contribué à accélérer. La technologie a offert au pouvoir des moyens, marquant la transition de la société disciplinaire foucaldienne vers la société du contrôle. Celle-ci se caractérise, outre par la saisie politique des corps et des esprits, dans le paradoxe de l’emprise sous consentement. Elle renouvelle en effet le contrat social en appelant à la proactivité du citoyen consommateur, dans le trompe-l’œil ce celui-ci. Si la puissance publique demeure le lieu d’exercice privilégié de ce biocontrôle, force est de constater qu’il n’en est plus le détenteur exclusif. La position dominante et globale acquise par les géants du numérique projette sur nos sociétés les nouveaux visages du pouvoir : du Léviathan à la Gorgone. Cette redistribution participe également du renouvellement du contrat social : sous l’effet de la standardisation technique qu’ils opèrent, ils s’imposent comme les nouveaux « points de contrôle » , au mépris voire au péril de nos démocraties.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.009
Scholarly communication0.0120.013
Open science0.0020.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0180.005

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 head0.032
GPT teacher head0.300
Teacher spread0.267 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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