How Networks of Empowerment Disrupt Persistent Unethical Behavior and Foster Organizational Change
Bibliographic record
Abstract
Unethical behavior persists within many workplaces despite internal policies designed to prevent it. Recent research found that one reason such behavior persists is because perpetrators formed networks of complicity that protected them, propagated increased unethical behavior, and created toxic organizational cultures. To investigate how such networks could be disrupted, we conducted 74 interviews in organizations where unethical behavior had persisted and was disrupted. In the face of organizational failure to disrupt persistent unethical behavior, we found that employees formed their own networks that we called “networks of empowerment.” Two theories emerged for our data: social network theory provided a foundation for our findings while behavioral ethics helped explain why employees joined various networks. These networks evolved over time and took three forms. Whisper networks typically emerged first to warn others about perpetrators. If a leader emerged, whisper networks could evolve into transactional networks that used informal and formal procedures such as filing complaints against perpetrators to achieve organizational change. When societal norms supported problematic organizational behavior, transformational networks formed to span organizational boundaries and address broader issues that impacted organizations and society. Our findings contributed to the above noted theories and research on organizational change, ethical leadership, and whistleblowing.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".