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Record W3133951545 · doi:10.3917/cliop.024.0071

Construire un savoir vivant de l’accompagnement de stagiaires en risque d’échec

2020· article· fr· W3133951545 on OpenAlexaff
Michelle Bourassa, Ruth Philion

Bibliographic record

VenueCliopsy · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

À partir du moment où on a un rôle de superviseur à l’égard d’une personne, fut-elle stagiaire, on est engagé dans une relation de soutien au développement de ses compétences professionnelles. Ce rôle nous installe de plain-pied dans un rapport de pouvoir. Pour tenir cette place, y guider chacun de nos pas de manière fine et différenciée, il nous faut prendre la juste mesure de ce que ce rapport installe entre nous et en nous. Notre recherche-action collaborative impliquant superviseurs et chercheurs donne à comprendre que soutenir le changement progressif visé par l’expérience des stages repose sur trois éléments : la confiance qui relève du lien qui s’installe entre stagiaire et superviseur pour, avant le stage, examiner ensemble les objectifs visés et les actions à entreprendre pour les atteindre, puis ensuite les ajuster au contexte. Avec la confiance, vient la bonne distance, celle qui nous fait échanger, ensemble, sur le sens à donner à ce qui se passe, incluant les effets que produisent gestes ou paroles afin, en nous ajustant à ses besoins, de tirer la ou le stagiaire vers ce qu’elle ou il peut advenir. Il y a enfin, la responsabilité de chacun puisque stagiaire comme accompagnateur partagent tous deux la charge d’accompagner.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
GPT teacher head0.423
Teacher spread0.220 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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