Disclosure transparency and impression management: A textual analysis of board gender diversity disclosures in Canada
Bibliographic record
Abstract
Abstract This article examines whether firms' board gender diversity disclosures signal transparency, reveal impression management, or both. To disentangle the two disclosure motives further, we also investigate whether the disclosure features of clarity and tone are related to new women director appointments. Based on initial gender diversity disclosures of 506 Toronto Stock Exchange (TSX) publicly traded firms, our results suggest that firms committed to enhancing board diversity provide clearer disclosures and exhibit a higher level of “optimism” to signal their confidence in the positive outcomes of diversity. In contrast, firms reluctant to enhance their diversity policies engage in impression management through obfuscation and show the most “certainty” in their diversity disclosures to indicate no change to the status quo is required. We also find that tone optimism does translate into enhanced diversity practices while certainty is negatively related to subsequent diversity performance. Taken together, our findings are consistent with the assertion that firms employ a combination of signaling and impression management strategies in their disclosures about diversity practices.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".