Strategic Silence and Discursive Repair: A Critical Genre Analysis of Negative Safety Disclosures in CSR Reports
Bibliographic record
Abstract
Corporate social responsibility (CSR) reports increasingly include negative disclosures acknowledging organizational failures and safety incidents, creating tensions between transparency obligations and reputation management. This study examines how Fortune 500 companies rhetorically construct negative safety disclosures using Critical Genre Analysis. Through systematic analysis of 20 safety disclosure sections from companies across five industries, we investigate rhetorical moves, interdiscursive practices, and cultural values characterizing these communications.The analysis reveals a standardized three-move rhetorical structure: “presenting the safety scene,” “reporting safety disclosure,” and “prompting action,” comprising ten constituent steps. Key findings demonstrate systematic interdiscursivity involving demonstrative, legal, and evaluative discourse types that transform regulatory compliance into strategic positioning opportunities. Three dominant cultural orientations emerge: altruistic culture extending safety benefits beyond organizational boundaries, human-centered culture emphasizing employee value, and science-based culture foregrounding technological sophistication.Results indicate companies strategically limit detailed incident reporting (15% of reports) while universally emphasizing safety commitments (100% of reports) and future endeavors (90% of reports). Negative disclosures function as complex rhetorical achievements rather than simple transparency exercises. These findings contribute to corporate communication research by illuminating how organizations balance competing stakeholder expectations while maintaining strategic advantage through sophisticated rhetorical strategies that simultaneously fulfill transparency obligations and advance corporate positioning.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".