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Record W4289717307 · doi:10.1051/pmed/2022005

Former et évaluer par concordance : des modalités éducatives complémentaires

2022· article· fr· W4289717307 on OpenAlexaff
Bernard Charlin, Nicolás Fernández

Bibliographic record

VenuePédagogie médicale · 2022
Typearticle
Languagefr
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesConcordancePhilosophyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Contexte et problématique : Les évaluations et les formations par concordance sont deux modalités éducatives qui reposent sur un cadre conceptuel commun illustré par la façon de poser les questions qui est commune à l’une et l’autre. Exégèse : Cette tribune plaide pour dire qu’il s’agit d’un couple dont les deux composantes sont optimisées si elles sont utilisées l’une et l’autre et qu’il est illogique de mettre en place le volet évaluation sans faire percevoir aux étudiants préalablement l’utilité du volet formation pour les faire progresser dans leur raisonnement. Enfin, la place que tient l’incertitude en matière de formation et évaluation par concordance est analysée.

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)0.060
metaresearch head score (Gemma)0.149
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.149
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.011
Scholarly communication0.0100.012
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.110
GPT teacher head0.416
Teacher spread0.306 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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