Managing Ethical Risks and Crises: Beyond Legal Compliance
Bibliographic record
Abstract
Recent interest in culture stems from its power to explain corporate and organizational failures. Such failures are both internal and external: accounting fraud, management misconduct, harassment and bullying in the workplace, racism, sexism, environmental issues, and health and safety concerns. Current theory holds that these failures are to be explained partly by the particular, poor organizational culture and unhealthy climate, poor leadership, and by the misdeeds of a few bad apples. When economic conditions are negative, organizations look to legislation, regulations, and codes, to reform their culture, and manage the risks of organizational failure. Both the compliance strategy, demanding obedience to laws, regulations and codes, and the integrity or values strategy, focusing on ethics training, education, tone at the top, and the hiring of employees with integrity and values, are the mainstay of recent legislation and regulations in North America and the European Union. We criticize the reliance on legislation, regulations and codes, the focus of a compliance solution which we find inadequate, ineffective, and unenforceable. We suggest reliance on a front-end, proactive and preventive program of best, precautionary practices, will better meet the challenge, in prosperity or poverty, of setting corporate culture on the right track.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".