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Record W7037249676

Doing the work? : The role of local government diversity, equity, and inclusion plans in addressing white supremacy

2024· other· en· W7037249676 on OpenAlexaboutno aff

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2024
Typeother
Languageen
FieldEngineering
TopicHuman auditory perception and evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsWhite supremacyLocal governmentInclusion (mineral)RacismGovernment (linguistics)Participant observationPublic policyPolitics
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.014
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.025
GPT teacher head0.239
Teacher spread0.214 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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