Measuring CSR Disclosure when Assessing Stock Market Effects
Bibliographic record
Abstract
ABSTRACT A growing number of studies are using a dichotomous variable indicating the presence of a standalone CSR report to capture impacts of CSR disclosure. Our concern is that, without considering differences in the information provided, such an approach could lead to incorrect inferences regarding those impacts. We extend prior research by examining whether, similar to differences in environmental disclosure, the mere presence of a standalone CSR report also mitigates negative market reactions at times of regulatory cost exposure. We focus on the 2011 Fukushima Daiichi disaster and a sample of international utilities with nuclear power generation. Controlling for other factors related to social and regulatory cost exposures, we find only the environmental disclosures appear to reduce negative market effects. We argue that, in exploring the impacts of CSR disclosure, researchers need to carefully consider, beyond just the presence of a CSR report, differences in the extent of information being provided.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".