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Record W2547178125 · doi:10.7202/1034438ar

Savoir scientifique, politiques gouvernementales et démocratie

2016· article· fr· W2547178125 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Review of Community Development · 2016
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’effet du savoir scientifique sur un ensemble de valeurs, définies de façon plutôt vague, que l’on désigne sous le nom de « démocratie », continue de susciter des passions et des analyses, comme en témoignent les revendications populaires en faveur d’une plus grande participation de la population aux processus de prises de décision relatives aux dossiers scientifiques. De telles revendications soulignent l’importance du débat politique dans des domaines qui furent jusqu’ici réservés à la recherche scientifique. Cependant, un débat éclairé ne peut avoir lieu que dans la mesure où les participants ont une certaine compétence dans le domaine technique, ne serait-ce que pour pouvoir évaluer les « impératifs techniques » des choix réels qui se posent. Le savoir scientifique — tout comme la terre, la force de travail et le capital — constitue une ressource, voire une marchandise. La possibilité d’utiliser et de contrôler cette ressource a des conséquences importantes sur la distribution du pouvoir politique dans les sociétés démocratiques.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.330
GPT teacher head0.391
Teacher spread0.062 · 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