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Record W4400649537 · doi:10.1108/edi-10-2023-0325

Diversity, equity and inclusion work: a difference that makes a difference … ?

2024· article· en· W4400649537 on OpenAlexaff
A. H. Armstrong

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEquity (law)Inclusion (mineral)Diversity (politics)Work (physics)Significant differenceDemographic economicsSociologyMathematicsPolitical scienceEconomicsGender studiesStatisticsEngineeringAnthropology

Abstract

fetched live from OpenAlex

Purpose I examine if current diversity, equity and inclusion (DEI) initiatives can actually accomplish what they aim and claim to do. I argue that perforce they cannot, as they remain instruments of capitalist corporations and other similar structures. Design/methodology/approach I draw on a variety of literature, from poetry to theories and to empirical findings. Findings DEI work so far does not live up to its hyped-up claims. It is time for scholars and practitioners to question the DEI industrial complex and its influence on organizational dynamics. It is not clear that justice can ever be achieved in a capitalist neoliberal economy. Research limitations/implications The paper is not an empirical paper. Practical implications DEI work needs to be re-conceived so that it addresses power imbalances, rather serving as a tool to keep organizations comfortable in seeming to change. Social implications DEI practitioners will need to draw deeply on their courage so that they do not reinforce the existing systems of capitalist oppression through their well-intentioned work. Originality/value The paper argues that DEI work can accomplish little without a radical reconceptualization of its nature as a genuine tool for change, rather than simply window dressing.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0490.000
Scholarly communication0.0010.001
Open science0.0010.372
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.374
Teacher spread0.271 · 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 designObservational
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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEquality Diversity and Inclusion An International JournalSame topicLabor Movements and UnionsFrench-language works237,207