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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 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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.110
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 teacher head, not a consensus.

Study designNot applicable
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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