Harming by Deceit: Epistemic Malevolence and Organizational Wrongdoing
Bibliographic record
Abstract
Research on organizational epistemic vice alleges that some organizations are epistemically malevolent, i.e. they habitually harm others by deceiving them. Yet, there is a lack of empirical research on epistemic malevolence. We connect the discussion of epistemic malevolence to the empirical literature on organizational deception. The existing empirical literature does not pay sufficient attention to the impact of an organization's ability to control compromising information on its deception strategy. We address this gap by studying eighty high-penalty corporate misconduct cases between 2000 and 2020 in the United States. We find that organizations use two different strategies to deceive: Organizations 'sow doubt' when they contest information about them or their impacts that others have access to. By contrast, organizations 'exploit trust' when they deceive others by obfuscating, concealing, or falsifying information that they themselves control. While previous research has focused on cases of 'sowing doubt', we find that organizations 'exploit trust' in the majority of cases that we studied. This has important policy implications because the strategy of 'exploiting trust' calls for a different response from regulators and organizations than the strategy of 'sowing doubt'.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".