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Record W7000008368

EDI AS A FORM OF INSTITUTIONAL ACTIVISM: CANADIAN UNIVERSITIES’ FIGHT AGAINST RACIAL INEQUALITY

2025· dissertation· en· W7000008368 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2025
Typedissertation
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsnot available
Fundersnot available
KeywordsInequalitySocial movementRacismSocial inequalitySocial changeStructural inequalitySocial issues
DOInot available

Abstract

fetched live from OpenAlex

Can university EDI offices actually reduce racial inequalities? Should we consider their efforts on social change to be activism? The question of eradicating inequalities has been at the centre of socio-political issues in our society in the last few decades. Universities have made commitments to eradicate - or at least reduce - inequalities as they project an image of progressiveness and inclusion. Accordingly, Canadian universities have promoted their EDI (Equity, Diversity, and Inclusion) initiatives more rigorously, especially in the wake of the proliferation of protest actions in support of Black Lives Matter (BLM) and #StopAsianHate. As universities began to show support for these social movements by providing official statements to the public, their efforts were construed as activist by right-wing pundits, while being regarded as largely symbolic and ineffective by scholars. Using insights from social movement theory and the sociology of education, I examine the possibilities and constraints of EDI offices in top English-speaking Canadian universities. In this case study of institutional approaches to racial inequality, I conduct in-depth interviews with seven of the top EDI officers in Canada and examine statements published by university officials to consider the social change goals and actions of U15 universities. I have three main findings. First, EDI leaders in Canadian universities cannot necessarily be considered social movement activists, but rather are best described as institutional mediators in advancing social issues toward social changes within institutions. Second, when universities make public statements about racial inequalities, and even when they make commitments to produce social change, their actions and claims fall short of activism, refraining from using the motivational framing of a call to action. Finally, I find that while adding resources to EDI offices does increase universities’ bureaucratic capacity to address racism, this institutional opportunity for social change does not translate into action on all anti-racism issues. Specifically, Canadian universities have been much more vocal and active in responding to demands of the Black Lives Matter movement than to the demands of the #StopAsianHate movement. I argue that the EDI practices of Canadian universities should not be considered a form of institutional activism, as I identify gaps between the institutionalized promotion of social issues and what would be considered a social movement agenda. This research contributes to the sociology of education literature, supporting its skeptical view about the effectiveness of EDI efforts within universities, while providing original insights on how universities’ EDI practices fall short of their stated goals in reducing racial inequality. In addition, it makes contributions to social movements theory’s understanding of organizational activism, expanding its understanding on the constraints on social change within organizations, impacting not only the outcomes of efforts to reduce inequality, but also the goals envisioned by EDI officers in the first place and restricting claims-making rhetoric. Overall, this research reflects that barriers embedded within universities based on institutional and cultural values are still main forces in delaying and hindering more progressive social change.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0530.027
Scholarly communication0.0140.003
Open science0.0030.010
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.016
GPT teacher head0.292
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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