What drives voluntary corporate water disclosures? <scp>T</scp>he effect of country‐level institutions
Bibliographic record
Abstract
Abstract The purpose of this cross‐country comparative research is twofold. First, it investigates the country‐level institutional factors that affect corporate incentives to voluntarily disclose water information. Second, it examines the way in which the interaction between formal and informal institutions affects corporate reporting decisions regarding water‐related risks. The results reveal that the country‐level legal system shapes the firm's propensity to voluntarily respond to the 2015 Carbon Disclosure Project water survey. Uncertainty avoidance and societal trust are negatively related to the propensity to provide voluntary water disclosures, whereas countries' future orientation is positively related to the likelihood of water reporting. More importantly, the results indicate that the effect of informal institutions on water disclosure practices is contingent on the strength of formal institutions at the country level. The paper contributes to the emerging water literature and provides insights on the effects of the interaction between formal and informal institutions on corporate behavior.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".