Raising the Bar and Closing the Gap? Investigating Learning for All’s Capacity to Support Marginalized Students in Ontario
Bibliographic record
Abstract
Learning for All (2013) is a resource guide, published by Ontario’s Ministry of Education, that aims to “raise the bar and close the gap in achievement for all students” (p. 3). It is intended to be used by school boards to support system-level planning and informs professional development and local policy directives (Ontario Ministry of Education, 2013). Learning for All does acknowledge that outcome disparities are more prevalent between certain demographic groups, but it avoids any discussion of the complex factors that cause this inequity. This paper explores the research on economically and racially marginalized students in Canada, to reveal the institutional, pedagogical, and ideological factors that produce this education inequity. From this research informed position, I offer a critical policy analysis of Learning for All guided by Paul Gorksi & Katy Swalwell’s Equity Literacy Framework (2015), which demonstrates that the strategies prescribed in Learning for All will not only fail to ‘close the gap’ but may also rein-force deficit thinking amongst educators, thereby exacerbating the problem. Finally, this paper concludes with recommended structural and pedagogical changes, as well as opportunities for future research to better address the barriers that marginalized students face and the shortcomings of Learning for All.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".