The Moderating Role of the Legal Context Between <scp>ESG</scp> Controversies, Economic Performance and Board of Directors
Bibliographic record
Abstract
ABSTRACT This study investigates the relationship between corporate governance mechanisms and ESG controversies in 300 publicly listed firms across six countries—Italy, Spain, France, the United Kingdom, the United States, and Canada—representing both Civil and Common Law systems. Using panel data from 2021 to 2023, the analysis explores how economic performance mediates, and legal context moderates, the impact of governance mechanisms on ESG failures. Findings reveal that the presence of ESG committees on boards is significantly associated with a reduction in ESG controversies. In contrast, board independence, gender diversity, and CEO duality show no consistent statistical effect. These results suggest that effective ESG oversight depends not only on the adoption of individual governance mechanisms, but also on contextual enablers such as financial health and regulatory environment. The study contributes to emerging debates on corporate accountability, offering policy‐relevant insights and a foundation for future research on ESG risk containment.
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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 | 0.018 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".