Race to Equity: Disrupting Educational Inequality
Bibliographic record
Abstract
The STAR group reached new ground when they fought for all schools in the Toronto School Board to provide copies of course outlines (which were already deemed to be public knowledge) in order to investigate and grade courses for their level of social justice content, including material from authors of color, material from gays and lesbians, and other social justice issues. Toward the Inclusive University, where he outlined six key principles that he felt essential in anti-racist education including: 1 anti-racist education dealing with the concept of racism being a social construction; 2 anti-racist education in the struggle for justice of oppressed groups, and institutional change resulting from political pressure; 3 anti-racist education could not be an add-on and changes were required across the curriculum; 4 anti-racist education needed to be system-wide; 5 anti-racist education had its own pedagogy that would require teachers and students to work together to understand and challenge unjust power relations; and, 6 anti-racist education must be willing to engage other forms of oppression including sexism, homophobia, and class prejudice that are all part of the education system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".