Doing the work? : The role of local government diversity, equity, and inclusion plans in addressing white supremacy
Bibliographic record
Abstract
Particularly post 2020, municipal governments have been under increased pressure to address racism in their communities and within their institutional structures. Many municipal governments have relied on DEI plans to guide this work yet these documents, and public policy on DEI at a local government level more generally, remain understudied. This thesis addresses this gap by examining the role that municipal DEI plans play in addressing white supremacy and advancing equity. I situate this study at the intersection of literature on DEI policy and social learning to think about how DEI policy at a local government level is developed, implemented, and the social learning processes that take place throughout. This research is conducted in Alberta, Canada and involves a document analysis of DEI plans, interviews with DEI practitioners and grassroots organizers, and participant observation. In my analyses, I show how DEI plans frame issues of racism and discrimination in ways that lack an ability to attend to inequitable institutional structures. I explore how DEI plans are simultaneously politicized by Councils and depoliticized through the downloading of work onto community all while being experienced as emotional work by DEI practitioners. Finally, I demonstrate the formalized approaches to learning outlined in DEI plans are insufficient to raise critical consciousness. I propose that a more explicit and supported praxis-oriented approach is needed.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".