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Record W2465980491 · doi:10.7202/1036032ar

La restitution comme espace de confrontation de savoirs pluriels : le cas d’une recherche-intervention

2016· article· fr· W2465980491 on OpenAlexvenueno aff
Marie-Madeleine Gurnade, Jean-François Marcel

Bibliographic record

VenueNouveaux cahiers de la recherche en éducation · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cette contribution étudie les interrelations entre la sphère scientifique et la sphère socio-politique à travers l’analyse de la confrontation de connaissances et de savoirs pluriels au cours d’une première restitution intermédiaire des résultats. Ce temps de présentation et d’échange avec les commanditaires (une municipalité) s’inscrit dans un observatoire du «vivre sa jeunesse». Dans le cadre d’une recherche-intervention, il s’agit de comprendre comment cette situation d’interaction concourt à la fois au développement d’un savoir partagé et à l’articulation des enjeux de la science et du politique. Pour ce faire, la sociologie de la traduction (Callon, 1989) et la théorie de la spirale de connaissances (Nonaka et Takeuchi, 1997) sont mobilisées pour interroger les discours produits dans le cadre de cette restitution. L’analyse sémantique et thématique des discours ainsi que des interactions met au jour les prémices d’un savoir partagé qui permet un réinvestissement dans la praxis.

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)0.024
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0130.030
Scholarly communication0.0100.010
Open science0.0020.012
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.472
GPT teacher head0.536
Teacher spread0.064 · 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 designQualitative
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

Citations5
Published2016
Admission routes1
Has abstractyes

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