Bibliographic record
Abstract
Given the recent increase in attacks on diversity, equity, and inclusivity (DEI) policies in North America and beyond, we explore the context surrounding recent changes to hiring policies for teachers in Ontario, Canada. The pressures to revoke policies are not new, and have been present in Ontario for many years, resulting in a roller coaster of policy enactments and revocations such as with Regulation 274/12 and PPM 165. The initial enactment of Ontario Regulation 274/12 that was intended to tackle issues of racism and discrimination in hiring practices was widely misunderstood and misconstrued by those who do not support DEI policies causing regulating bodies to publish articles debunking myths surrounding the policy. However, misconception and lack of support remained, which encouraged the Ontario Government to inevitably revoke PPM 165. In this article, we explore the social and political context surrounding the policies and the policy changes to help the reader understand the true purpose and utility of policies such as Regulation 274/12. It is our hope that administrators and hiring committees will find this information helpful as they attempt to hire teachers for their schools. Given the current political climate, this type of exploration is more important than ever.
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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".