Bibliographic record
Abstract
In this chapter, I articulate a multilayered, anti-classist agenda for teachers, teacher educators, and K-12 educational policymakers. While locating the persistence of educational inequality in power differentials, I reject cultural-deficit rationales used to justify why low-income and working-class children so often fare badly in schools. Instead, I advocate for an asset-based approach founded on high expectations for working-class students, one that challenges damaging myths and stereotypes and other pervasive forms of the class bias that permeates schooling. Informed by scholarship on educational “risk” within the intersecting contexts of classism, racism, and other forms of institutional injustice, and mindful of lessons learned over two decades of community-driven, equity-focused educational activism in Toronto, Canada, I recommend that educators adopt a series of anti-classist principles in their work by pursuing strategies ranging from changing how we recruit, educate, and mentor teachers, to critiquing and correcting biased learning materials, to developing robust school board human rights policies. If these recommendations were to be implemented, working-class students would be far more likely to realize their right to socioeconomically just, meaningful, and high-quality schooling.
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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 | 0.001 | 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 | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".