Whose ‘truth’ matters? Problematizing the epistemological underpinnings of social policy research
Bibliographic record
Abstract
Despite many policies produced to mitigate racism and other forms of discrimination in the public education sector, the inability to implement these policies fall short. These policies may appear to address inequities by attempting to meet legal requirements, however, they do not always address the structural power dynamics grounded in white supremacy which continue to perpetuate systemic racism. Drawing on examples from the Ontario public education sector such as employment equity policies, Afrocentric Alternative Schools in the TDSB and the Student Resource Officer program in Toronto schools, I illustrate how positivist inspired “evidence-based” policy processes fail at offering policy solutions for equity issues by ignoring those who do not fall into the purview of the economic and political interests of dominant stakeholders in the policy process. Deploying an anti-racist and anti-colonial framework, I examine the superficial attempts to garner inclusivity through equity policies that continually fail to heal the wounds of exclusion by reproducing multiple band-aid policies rather than formulating a restructuring of public education in the Ontario public education system that will allow equity seeking groups a place at the table.
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 | 0.228 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.018 | 0.257 |
| Scholarly communication | 0.034 | 0.043 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".