Bibliographic record
Abstract
Issues of equity and social justice are particularly acute in the Canadian province of Saskatchewan, where massive disparities in income, employment, health, educational achievement, incarceration rates, and other indices of well-being currently separate Aboriginal peoples from the non-Aboriginal majority. In Saskatchewan, poverty is a racialized phenomena and is addressed here as a race/class intersection. In this chapter, we briefly trace the historical roots and current dimensions of these challenges. The bulk of the chapter addresses recent strategies designed to improve educational outcomes for Aboriginal students as a means of fostering greater equality between the two racial solitudes in the province. While emphasizing the critical role of publicly funded education as a means of mitigating disadvantage and promoting social cohesion, we draw on insights from postcolonial and neo-Marxist theory to argue that schools alone cannot effect the larger social and structural changes required to eliminate the racialization of poverty in Saskatchewan. Acknowledging that our colonial past continues to inform current disparities requires a painful confrontation with the realities of white racial privilege and necessitates a more equitable and ethical distribution of wealth than currently premised under neoliberal fiscal, social, and educational policies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".