Deconstructing inclusive STEM education: Understanding the racialized and gendered experiences of women of colour in secondary STEM education in Canada through counter-storytelling
Bibliographic record
Abstract
Les femmes racisées restent exclues des domaines des sciences, de la technologie, de l’ingénierie et des mathématiques (STIM) malgré les efforts pour promouvoir l'équité, la diversité et l’inclusion. Les recherches existantes souvent adoptent une thèse du déficit et négligent l’intersection de la race et du genre. Cette recherche se concentre sur les expériences des femmes racisées en éducation STIM dans l’école secondaire au Canada, utilisant la théorie critique de la race et la méthodologie du contre-récit pour explorer ces intersections. Utilisant les histoires de neuf femmes, trois d’Asie de l’Est, trois d’Asie du Sud et trois Noires, cette recherche remet en question les efforts d’équité qui reflètent les intentions néolibérales et donnent la priorité à l’avantage de l’État plutôt qu’aux individus des groupes marginalisés. Les histoires individuelles et collectives des femmes racisées illuminent les discours sur la race et le genre, incluent le discours du mythe de la minorité modèle et l’anti-noirité dans le contexte de l’enseignement de STIM au Canada
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".