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Record W2767710745 · doi:10.4000/books.pum.4295

État, pouvoir et science

2015· book-chapter· fr· W2767710745 on OpenAlexafffund
Stéphane Castonguay

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2015
Typebook-chapter
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversity of Illinois at Urbana-ChampaignOrganisation de Coopération et de Développement ÉconomiquesUniversity of TorontoJohns Hopkins UniversityPrinceton UniversityUniversity of OxfordHarvard UniversityUniversity of CambridgeWayne State UniversityState University of New YorkStanford Bio-XStyrelsen för Internationellt Utvecklingssamarbete
KeywordsAppropriationPolitical scienceHumanitiesPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Source de prestige pour les princes de la Renaissance, la science est devenue avec le développement des États-nations source de pouvoirs multiples, touchant à la fois les sphères militaires, économiques et sociales. Si les historiens se plaisent à retracer dans les machines de guerre d’Archimède pour le roi Hiéron une première alliance entre science et pouvoir, tandis que les philosophes récitent les aphorismes de Francis Bacon et René Descartes qui ont, chacun à leur façon, envisagé le pouv...

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.995
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.021
Scholarly communication0.0090.008
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.004

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 head0.115
GPT teacher head0.248
Teacher spread0.133 · 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.

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
Published2015
Admission routes2
Has abstractyes

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