Information misbehavior: How organizations use information to deceive
Bibliographic record
Abstract
Abstract Recent examples of organizational wrongdoing such as those that led to the opioid crisis and the 2008 financial meltdown show that organizations can deliberately use information to deceive others, resulting in serious harm. This brief communication explores the role of information in organizational wrongdoing. We analyze a dataset consisting of 80 cases of high‐penalty corporate wrongdoing in the United States in the period 2000–2020. Our analysis of documents filed by the US Department of Justice and federal regulatory agencies in those cases found that organizations use two general information strategies to deceive and mislead. First, organizations can “sow doubt” on statements by others that hurt the organization's interests. Second, organizations can “exploit trust” that others have placed in them to provide truthful information. Our analysis suggests that which strategy is adopted depends on the degree that the organization's external information use environment is “contested” or “controlled.” Across the cases examined, we observe three types of information behaviors that implement the strategy of sowing doubt and exploiting trust: information obfuscation, information concealment, and information falsification.
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.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".