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? \nUsing 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".