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Record W4401877758 · doi:10.17118/11143/21850

Concours de vulgarisation scientifique 2024

2024· book· fr· W4401877758 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Le Concours de vulgarisation scientifique consiste à soumettre une courte nouvelle vulgarisée ou une bande dessinée destinée au grand public portant sur un chapitre de livre ou un article publié ou accepté pour publication dans une revue scientifique avec comité de lecture par des étudiants ou des professeurs de l'Université de Sherbrooke. La nouvelle ou la bande dessinée doit être basée sur un article de recherche présentant des résultats originaux publié ou accepté pour publication dans une revue scientifique avec comité de lecture ou un chapitre de livre publié ou accepté pour publication entre le 1er janvier 2023 et le 30 novembre 2023 dont les auteurs sont des étudiantes, des étudiants, des professeures ou des professeurs de l’Université de Sherbrooke. L’accord de l’auteur principal (et de celui de l'UdeS, si l’appartenance de l’auteur principal n’est pas l'UdeS) doit avoir été obtenu afin de pouvoir participer au concours.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0020.005
Scholarly communication0.0090.006
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0600.041

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 head0.041
GPT teacher head0.355
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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Same topicFrench Language Learning MethodsFrench-language works237,207