Educational equity in Canada: the case of Ontario’s strategies and actions to advance excellence and equity for students
Bibliographic record
Abstract
Canada prides itself for being multi-cultural, valuing diversity, and having educational outcomes that have been identified as excellent and equitable with above average performance and lower than average impact of socio-economic status and immigrant status in PISA. The Canadian Charter of Rights and Freedoms, plus policies concerning child care, language rights, immigration, and Indigenous people have affected equity. However, there are long-standing and emerging inequities, particularly for Indigenous people. Within this context, this article examines the case of Ontario, a province which has become well-known for educational excellence and equity. Two main strands of system-wide strategies to advance educational equity are discussed. First, a focus on closing the gaps in educational achievement and improving student success. This strategy resulted in improved performance for students overall and reduced gaps in performance for sub-groups of students, including attention to gender, English Language Learners and Special Education Needs. However, these measures did not fully address other demographic factors, systemic inequities and multiple forms of discrimination. A second strand of work was developing strategies and actions to advance an equitable and inclusive education system, including a broader concept of equity to support students and staff with changes in classrooms, schools, districts and the province.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.053 | 0.013 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".