La dynamique de l'engagement chez des étudiantes en formation des maîtres analysée sous l'angle des états identitaires
Bibliographic record
Abstract
L’engagement est un facteur important d’affirmation identitaire sur le plan professionnel. Dans le cadre d’une étude portant sur la construction de l’identité professionnelle d’enseignants en formation des maîtres et des moyens curriculaires y contribuant, une analyse qualitative des données a été effectuée en regard de l’engagement de finissantes du baccalauréat en éducation préscolaire et en enseignement primaire, particulièrement quant au choix de la profession enseignante. Cette analyse a été effectuée en utilisant la théorie des états identitaires de Marcia. Les résultats proposent que l’engagement diffère chez les sujets en fonction de leur état identitaire. Des pistes sont suggérées concernant des dispositifs de formation favorisant l’engagement. Mots clés : engagement, étudiant, formation des maîtres, état identitaire Commitment is an important factor in the affirmation of professional identity. As part of a study on building professional identity in student teachers and the ways the curriculum can contribute to this process, we carried out a qualitative analysis of the commitment of graduating students in preschool and primary teacher education programs, with special reference to their choice of the teaching profession. We based our analysis on Marcia’s identity status theory. The results suggest that the subjects’ commitment varies according to their identity status and we make suggestions for teacher training programs that encourage commitment. Key words: commitment, teacher education students, identity status
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".