A Scoping Review of the Demographic and Contextual Factors in Canada's Educational Opportunity Gaps.
Bibliographic record
Abstract
Despite widespread discussion in the United States, up until now there has not been a review of the demographic and contextual factors associated with Canadian academic achievement. Using Arksey and O’Malley’s (2005) framework, a scoping review was conducted to answer two questions: What demographic and contextual factors are most commonly used in K–12 academic achievement studies in Canada? What, if any,research gaps exist? Fifty-four studies were identified for review. The results reveal 40 demographic or contextual factors, with socio-economic status (SES), gender, language factors, immigrant status, family structure, and Indigenous status being the most commonly studied. Race, religion, and LGBTQ+ identity were understudied factors. The authors recommend the adoption of “educational opportunity gap” as a consistentresearch term, identify understudied factors, and outline several research design considerations.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.046 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".