Reliably unreliable: exploring the signals sent by non-profit sport governing bodies’ diversity, equity, and inclusion policies
Bibliographic record
Abstract
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.
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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 | 0.038 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".