« Un élève comme nous » : apports d’une perspective interactionniste stratégique pour comprendre le point de vue d’élèves au secondaire sur l’expérience d’avoir un stagiaire en enseignement
Bibliographic record
Abstract
Les etudiants en formation a l’enseignement vont dans les ecoles pour faire l’apprentissage du metier et considerent ces moments comme les plus significatifs de leur formation, mais on en sait bien peu du point de vue des eleves. A partir d’une perspective theorique, interactionniste et strategique, les notions de point de vue et de repertoire sont convoquees pour apprehender les multiples experiences des eleves. Les discours de 24 eleves du secondaire, produits dans le cadre d’entrevues, sont presentes afin d’apprecier le potentiel de cette perspective theorique. De nouvelles voies de recherche sont tracees pour elargir la comprehension des stages considerant le point de vue des eleves. Abstract Preservice teachers go into schools in order to learn how to do their jobs, and they consider these times to be the most significant ones of their learning process. However, we know very little about the students’ points of view. From a strategic interactionist perspective, points of view and repertoire are used to capture the students’ multiple experiences. To appreciate the potential of this theoretical perspective, the discourse of 24 students, obtained from interviews, are presented and discussed. This new research takes into account the students’ points of view and broadens our understanding of the students’ practicums.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".