A Critical Policy Analysis of the Ontario Equity and Inclusive Strategy: The Dynamics of Non-Performativity
Bibliographic record
Abstract
Institutions of higher education across Ontario are increasingly expressing their commitments to diversity and inclusion through the development of various initiatives, including the implementation of policies that elucidate institutional promises of equity.Few studies have examined such policy efforts in Canadian higher education, but we suggest that insights into school board policies can help to inform a critical analysis of equity policies in universities.This paper is part of a larger project that investigates the enactment of Ontario's equity and inclusive education strategy across all school boards in the province.In 2009, the Ontario government mandated all 72 school boards to develop a policy on equity and inclusive education.Drawing on theories of critical policy analysis, this paper provides an analysis of the policies drafted by eight school boards in southwestern Ontario during 2019-2020.Our analysis suggests that these policies largely follow verbatim transcriptions of the Ontario Ministry of Education's equity policy, and fail to construct localized policies that include procedures, enactment strategies, and evaluation methods that respond to existing challenges within each local context.Drawing on the work of Sara Ahmed (2012), and based on our review of policy documents, our analysis suggests that equity policies function to protect the institution and its image rather than challenging institutional inequities.Ultimately, we argue that these policies are "nonperformative" and fail to address systemic inequities in the education system.The implications of this for higher education will be discussed.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".