Managing Ethical Risks and Crises: Beyond Legal Compliance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it