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Record W4415945764 · doi:10.1080/16184742.2025.2578534

Reliably unreliable: exploring the signals sent by non-profit sport governing bodies’ diversity, equity, and inclusion policies

2025· article· en· W4415945764 on OpenAlexaffabout
Erik L. Lachance, Jeffrey D. MacCharles, Shannon Kerwin

Bibliographic record

VenueEuropean Sport Management Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock University
Fundersnot available
KeywordsInclusion (mineral)Corporate governanceFinancial inclusionAthletesPublic policyQualitative research

Abstract

fetched live from OpenAlex

Research Question The purpose of this study was to explore the signals sent from diversity, equity, and inclusion (DEI) policies in non-profit SGBs. Two research questions were posed: what signals are non-profit SGBs sending with the content of their DEI policy titles? and how reliable are signals according to the meaning communicated in non-profit SGBs’ DEI policy purpose statements?Research Methods With signaling theory as a frame, documents represented the data source. The websites of 757 Canadian non-profit SGBs were consulted to collect DEI policies, whereby a content analysis (i.e. quantitative and qualitative) was conducted using NVivo 14.Results and Findings Results identified 229 DEI policies within 185 non-profit SGBs, representing 24% of the sample. Within the collected DEI policies, 46 unique title combinations were found compared to 26 unique purpose statement meanings.Implications Signal unreliability between the titles and purpose statements is present in non-profit SGBs, suggesting the signal sent by DEI policies is inconsistent and may confuse receivers. The results emphasize the utility of signaling theory in relation to DEI policies in a federated non-profit SGB context.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score1.000

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 studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.012
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 head0.032
GPT teacher head0.298
Teacher spread0.266 · 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
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

Citations1
Published2025
Admission routes2
Has abstractyes

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