Does Board Political Diversity Stimulate Environmental Innovation?
Bibliographic record
Abstract
In this study, we investigate both why and in what contexts board political diversity is associated with environmental innovation. Drawing from upper echelons theory, political psychology, and stakeholder engagement theory, we explore the mediating role of stakeholder orientation, and moderating roles of diversity and inclusion culture, ESG controversies, and liberal state locations to explain the relationship between board political diversity and environmental innovation. We propose that a more politically diverse board is associated with increased environmental innovation because boards with political diversity prioritize stakeholder engagement and environmental issues. We test the hypotheses using a large panel data set with the S&P 1500 firms over the period of 2002-2018 and find evidence supporting the hypotheses. The results suggest that stakeholder orientation is a key mechanism through which a politically diverse board is positively associated with environmental innovation. There is also a synergistic interaction effect between a politically diverse board, diversity and inclusion culture, ESG controversies, and state political liberalism which facilitates environmental innovation.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".