Perspectives entourant la saisie de l’expérience enseignante lors d’incidents face-à-face pouvant perturber le climat de la classe
Bibliographic record
Abstract
L’accès aux savoirs en action reste un défi tant pour les professionnels eux-mêmes que pour les chercheurs. Dans le cadre de cet article, les incidents sont abordés puisqu’ils permettent de questionner les pratiques et les valeurs sous-jacentes à ces savoirs en action. L’objectif de cette contribution est donc d’explorer l’empan des possibilités associées à la saisie de l’expérience enseignante lors d’incidents. À la suite d’une recension des écrits sur les incidents, deux perspectives liées à la façon de saisir les schèmes ainsi que les indices guidant la perception semblent orienter les écrits. Certains se concentrent sur le traitement de l’information et considèrent la perception comme étant préalable à l’action. D’autres reconnaissent la perception de l’acteur, son ressenti et son intuition lors d’incident. Mots-clés : incident; expérience; savoir professionnel; pensée enseignante; perception; recension des écrits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".