The influence of board interlocks and sustainability experience on transparent sustainability disclosure
Bibliographic record
Abstract
Abstract We investigate board interlocks and their relationship to the transparency of sustainability disclosure, drawing on the theoretical perspectives of resource dependence theory and agency theory. We ascertain that board members who gain sustainability experience by serving on another board will influence the transparency of sustainability disclosure for the focal firm. The study analyzes data from S&P 1500 firms in the U.S. from 2009 to 2018, using ordinary least squares regressions. Our findings demonstrate that the focal firms' sustainability disclosure will be more transparent if their boards have interlocking directors with experience gained from other boards in current or prior years. Furthermore, we find that the sustainability experience of interlocked firms interacts with both gender diversity and board independence, leading to an enhancement in the transparency of sustainability disclosure. In addition, we conduct robustness tests such as performing propensity score matching, controlling for firm fixed effects, and applying entropy matching. These additional tests provide consistent results to confirm and strengthen our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".