The Making of An Echo Chamber: A Critical Discourse Analysis on Equity, Diversity, and Inclusion (EDI) Educational Policies Across Public Schools in Greater Toronto Area, Ontario
Bibliographic record
Abstract
Under the legislation of Ontario Ministry of Education's PPM 119: Developing and Implementing Equity and Inclusive Education Policies in Ontario schools (2009), Equity, Diversity and Inclusion (EDI) practices and policies has been widely promoted across public education institutions in Ontario over the past decade. These policies and practices can be situated in Canada’s neoliberal and colonial context, where institutional antiracism work has been diluted as a celebration of diversity and multiculturalism, which has historically been a symbolic marker of Canada's nationhood and citizenship. These efforts led to the adoption of outcome-focused, individualized, performative policies. In wake of a recent tragic incident of a TDSB principal's suicide, its legal dispute linking the death to an antiracism training session in the workplace, as well as the consequent responses from media outlets and the public, this study presents a critical discourse analysis of the dominant institutional EDI culture and how it has led, or rather, impeded the development of antiracism across Ontario's public-school sector. Entering from a scan of the media responses reporting on the incident and its harshest critics about EDI programs, this study follows Fairclough's (1995) critical discourse analysis methodology in the efforts to trace back the lineage of today's institutional EDI programs from Canada's longstanding liberal multiculturalism agenda. Through analyzing documents of texts, images and videos detailing EDI policies and action plans, collected from the 4 largest public-school boards in the Greater Toronto Area, this study found an overwhelming echoing sentiment and neoliberal discourses with the state’s liberal multiculturalism and antiracism rhetoric. Last, this study attempts to reimagine the trajectory of antiracism work with a focus from a decolonial lens.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.050 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".