Diversity statements for leveraging organizational legitimacy
Bibliographic record
Abstract
European companies are increasingly putting “diversity statements” on corporate websites. Websites are important because they are used by members of the public, especially the younger generation, to seek information about companies. Legitimacy theory is often cited as one explanation for having good diversity policies, but we found no research in the diversity, HRM or social accounting literature with empirical evidence of different types of legitimacy associated with diversity. We examined on-line diversity statements from 174 top European companies for evidence of legitimacy-enhancing messages, and coded them by type of legitimacy. We show that diversity statements are presented in ways associated with two different types of legitimacy (pragmatic exchange and moral). International differences are also highlighted. These findings will help practitioners to design diversity statements based on a better understanding that legitimacy is a multi-faceted construct, and help them avoid the dangers of empty discourse, i.e. inconsistency between words and reality.
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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 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".