Inequities in Black et Blanc: Textual Constructions of the French Immersion Student
Bibliographic record
Abstract
This thesis is an investigation into Toronto and Ontario French immersion policy, curricula, and other related documents in order to understand if and how current documents contribute to the over-representation of White middle-class students in French immersion. The study found that French immersion policies, curricula, and documents evidenced a middle-class bias due to lack of resources, transportation and promotional materials, and location of programs. It had a White bias through its Eurocentric curricular content. The documents of this study also privileged English above all other languages, and families who were not newcomers. The documents did not entirely mirror the population in French immersion in terms of gender, home language and special education needs. Equity documents highlight that steps are being taken toward a more inclusive immersion program, but there is still much to do, especially in terms of dismantling and transforming hierarchies.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".