Corporate Conduct in the time of Callouts: How Public Criticism Shapes Environmental Disclosure and Outcomes
Bibliographic record
Abstract
This paper investigates the effects of firm-targeted greenwashing criticism from the public via social media on environmental disclosure sentiment, or talk, and emissions-based performance, or walk. I apply a novel approach for identifying greenwashing using machine learning and targeted sentiment analysis to assess the tone latent in scripted environmental narratives of conference calls. The primary findings indicate that firm-targeted criticism events, or callouts, on Twitter (X) have a near-term disciplining effect on the disparity between a firm’s talk and its walk, which I call the talk-walk gap. The decrease in the talk-walk gap occurs after both first-time and subsequent callouts, is more pronounced in firm-quarters immediately following callouts, and is driven by a reduction in the sentiment of scripted environmental disclosures. Over longer horizons, I observe an improvement in walk, reflecting a decrease in the emission intensity of called-out firms. For targeted firms in the post-callout period, the likelihood of environmental discussion by managers and analysts does not change significantly, analysts pose environmental questions with more negative sentiment, and neither institutional nor green institutional investors divest their holdings. Overall, I find evidence that firms adjust their behavior after being called out; first, through dampened disclosure sentiment and subsequently, through lower emissions. These findings underline the modern social media callout phenomenon and the capacity of the public to influence corporate disclosure and environmental outcomes.
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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 | 0.002 | 0.025 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".