Corporate social responsibility signalling under external transparency demands
Bibliographic record
Abstract
Drawing on the premise that Corporate Social Responsibility (CSR) expenditures may contain valuable private information about future financial outcomes, we explore the conditions necessary to decode this signalling component. Given that monitoring fosters credibility and trust, we posit that increased external pressures for transparency encourage investors and creditors to perceive the private information embedded in CSR reports. Given the heterogeneity of external transparency within and across countries, we employ both a firm-level proxy that minimizes firm-specific incentives as well as country-level proxy based on two exogenous shocks. We resort to the adoption of the International Financial Reporting Standards (IFRS) and the implementation of the EU's mandatory CSR transparency regulation, Directive 2014/95/EU, to capture country-level external transparency. Our findings indicate that the positive signalling effect of CSR expenditures is strongly linked to a reduced likelihood of financial constraints, with external transparency playing the driving role. • Companies use CSR numbers to signal private information. • The perception of hidden information requires some degree of trustworthiness. • External Transparency, independent of firm-specific incentives, builds trustworthiness. • Debt holders are more aware of hidden information in CSR numbers than equity holders.
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 | 0.006 | 0.047 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".