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Record W4366816844 · doi:10.37571/2023.0203

Engagement en formation professionnelle initiale duale et perceptions de la qualité de la formation

2023· article· fr· W4366816844 on OpenAlexvenueno aff
Jean‐Louis Berger, Matilde Wenger, Florinda Sauli

Bibliographic record

VenueDidactique · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

L’engagement en formation constitue l’un de moteurs de l’apprentissage et de la réussite d’une formation. Cet engagement mérite d’être examiné pour en comprendre les sources et les conséquences au travers de divers contextes formatifs. Dans le cas du contexte de la formation professionnelle initiale duale en Suisse, les facteurs liés au degré d’engagement des apprenti·es sur les deux principaux lieux de formation (entreprise formatrice et école professionnelle) sont méconnus. Ainsi, cette étude avait pour objectif d’examiner comment les perceptions des apprenti·es quant à la qualité de leur formation peuvent expliquer leur engagement dans chacun des deux principaux lieux de formation. Les analyses ont permis de confirmer que les perceptions de la qualité de la formation prédisaient l’engagement au-delà de multiples caractéristiques individuelles ainsi que d’aspects motivationnels. Les résultats permettent de conclure à la pertinence de tenir compte de la façon dont les apprenti·es considèrent la qualité de leur formation pour constituer d’éventuels leviers sur lesquels agir pour soutenir leur engagement.

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)0.016
metaresearch head score (Gemma)0.037
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0100.004
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.117
GPT teacher head0.501
Teacher spread0.384 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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