Do socially responsible firms provide more readable disclosures in annual reports?
Bibliographic record
Abstract
Abstract This paper examines the relationship between the adoption of corporate social responsibility (CSR) practices and the syntactic complexity of the management's discussion and analysis (MD&A) section of the annual report. Based on stakeholder and agency perspectives, we offer an empirical test of two competing hypotheses. First, we may expect socially responsible firms to provide transparent disclosures because this reflects a firm's commitment to high ethical standards. In contrast, the agency perspective predicts that managers engage in CSR for self‐interest purposes and that CSR‐oriented firms will be more likely to attempt to mislead stakeholders about the firm's actual performance through complex narrative disclosures. Based on a sample of large firms listed on the Toronto Stock Exchange, our results show a positive association between corporate social performance and the MD&A's textual complexity. Consistent with the agency perspective, our findings suggest that managers may engage in CSR opportunistically and use complex narrative disclosures in an impression management strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".