Perpetuating Inequities in Ontario Schools: A Large-Scale Practice of Assessment
Bibliographic record
Abstract
The purpose of this study was to explore the notion that there are prejudices in the schools system that affects non-standard English speakers and to analyze how this may affect the process of assessment. The primary question guiding this study was: How is a small sample of Junior/Intermediate teachers in the Peel District School Board (PDSB) addressing issues of inequitable assessments and culturally relevant testing in their classrooms? Using semi-structured interviews I interviewed three teachers from PDSB. The themes uncovered in this study included, issues related to ethnicity, teacher education, and assessment practices. I found the potential for a disconnect between the inclusive teaching and learning mandate provided by the school boards and what takes place in classrooms. Moving forward I suggest that initial teacher training be enhanced to include culturally responsive approaches to assessment in both classrooms and in the full-scale assessment practices across Ontario.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".