The Uneven Returns of Transparency in Voluntary Nonfinancial Disclosures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Voluntary nonfinancial disclosures are an increasingly relevant element of corporate sustainability strategies. Despite their importance, research is conflicted on how the transparency of such disclosures affects market and nonmarket outcomes. A possible reason is that transparency consists of multiple dimensions, each of which may be valued differently by market and nonmarket actors. Drawing on insights from attribution theory, we explore the effects that different information traits of voluntary disclosures have on market and nonmarket actors. We suggest that the completeness, clarity, and accuracy of voluntary nonfinancial disclosures affect both market (i.e., market valuation) and nonmarket (i.e., reputation risk) reactions. Using data from the Carbon Disclosure Project, we find that these actors react differently to three distinct dimensions of transparency: completeness, clarity, and accuracy. Our findings highlight the importance of the nuanced relationship between transparency and market and nonmarket actor reactions, which has implications for broader, sustainability-related outcomes.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it