ENVIRONMENTAL REPORTING ON THE INTERNET BY AMERICA'S TOXIC 100: LEGITIMACY AND SELF-PRESENTATION
Bibliographic record
Abstract
This study uses Goffman's self-presentation theory to examine corporate website environmental disclosures from an organizational legitimacy perspective. We argue that corporations use Internet environmental disclosure to project a more socially acceptable environmental management approach to public stakeholders. We argue further that this disclosure activity is often de-coupled from their actual environmental performance. To test these conjectures, we refine and employ a comprehensive disclosure evaluation metric to assess both the content and the presentation of these types of disclosures and utilize a firm's America's Toxic 100 toxic score - a newly developed measure based on the US Environmental Protection Agency's toxics release inventory (TRI) data, to proxy for environmental performance. Based on empirical tests of four size-matched samples, our findings support our conjectures, showing that worse environmental performers provide more extensive disclosure in terms of content and website presentation.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| 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 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".