Enseigner en milieu minoritaire: histoires d'enseignantes oeuvrant dans les écoles fransaskoises
Bibliographic record
Abstract
Cet article traite d'enseignantes qui oeuvrent dans les écoles fransaskoises et qui partagent avec nous certaines de leurs histoires d'enseignement. Il en ressort que ces enseignantes prennent à coeur la mission de l'école minoritaire, mais qu'elles semblent rencontrer des difficultés à vivre et à faire vivre à leurs élèves des histoires relatives aux aspects plus politiques de cette mission. En ce sens, il leur reste à pouvoir se raconter, à vivre et peut-être même à devoir inventer des histoires touchant à la conscientisation, la transformation et la libération de ces derniers du déterminisme socio-linguistique auquel plusieurs d'entre eux semblent astreints. Globalement, l'étude permet de mieux comprendre comment ces enseignantes vivent leur expérience de travail en milieu minoritaire.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".