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Record W4312638046 · doi:10.7202/1092846ar

Évaluation qualitative de trois programmes de mentorat dans des institutions d’enseignement supérieur

2022· article· fr· W4312638046 on OpenAlexaffvenue
Al Hassania Khouiyi, Danielle St-Amand, François Guillemette, Jason Luckerhoff, Marie-Josée St-Pierre, Mamadou Siradjo Diallo

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2022
Typearticle
Languagefr
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article présente les résultats de trois projets de recherche qui portaient chacun sur l’évaluation d’un programme de mentorat dans une institution québécoise d’enseignement supérieur. Les recherches portaient plus particulièrement sur le vécu des personnes impliquées dans la dyade mentor-mentoré. Dans le premier programme évalué, la relation mentorale est vécue entre un professionnel enseignant et un étudiant (professionnel en formation). Dans le deuxième programme évalué, cette relation est vécue entre un enseignant expérimenté et un enseignant novice. Dans le troisième programme, la relation en est une entre pairs, c’est-à-dire entre deux enseignants de manière non hiérarchique. Les résultats montrent qu’en dépit des différences dans la posture mentorale, ces programmes sont appréciés par les mentors et les mentorés qui en tirent des bénéfices réciproques sur le plan humain aussi bien que pour leurs pratiques professionnelles.

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)0.030
metaresearch head score (Gemma)0.053
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 score0.030
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.134
GPT teacher head0.443
Teacher spread0.309 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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