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Record W4321144138 · doi:10.1108/edi-02-2022-0048

Why do EDI policies fail? An inhabited institutions perspective

2023· article· en· W4321144138 on OpenAlexaffabout
Roger Pizarro Milian, Rochelle Wijesingha

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsInefficiencyBureaucracySkepticismPublic relationsOriginalityPolitical sciencePerspective (graphical)Inclusion (mineral)SociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Purpose Equity, diversity and inclusion (EDI) policies have proliferated in recent decades, but studies have repeatedly noted their inefficacy and adverse effects. To understand the potential root causes of the inefficiency of EDI policies, this study examines how they are inhabited by individuals at the ground level. Design/methodology/approach This study draws on data gathered through 23 in-depth interviews with instructors at Progressive U , a large research-intensive Canadian university. Findings The data gathered/analyzed suggest that the implementation of EDI policies at Progressive U is hindered by the absence of coercive enforcement mechanisms, skepticism about their authenticity, the over-regulation of work and unresponsive bureaucratic structures. Originality/value This study examines the implementation of EDI policies through the prism of the inhabited institutions perspective in organizational sociology, producing insights that help to explain why EDI policies typically fail. In doing so, it produces insights relevant to both academic researchers and practitioners in the field.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0220.001
Scholarly communication0.0000.003
Open science0.0010.010
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.197
GPT teacher head0.404
Teacher spread0.207 · 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 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

Citations27
Published2023
Admission routes2
Has abstractyes

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