Changes to Corporate Codes of Ethics: A Twelve-Year Analysis
Bibliographic record
Abstract
The Enron scandal caused companies and their Board of Directors to reconsider how they were utilizing their code of ethics, especially after the legislation of the Sarbanes-Oxley Act of 2002. Enron's Board of Directors provided the CFO, Andy Fastow, with a waiver of the code of ethics to negotiate with himself, while also on behalf of Enron. The issue with this waiver was that, at the time, investors were left in the dark because they did not need to be notified about any changes or exceptions made to the code of ethics. After learning about why codes of ethics and any changes to them needed to be disclosed, I looked at all the changes made to companies' codes of ethics over the last twelve years and classified them. I classified the types of changes into either a waiver, an amendment, a new code of ethics, or other. From the classified data, I was able to discover two main trends: most changes are made during the fourth quarter of the year, and the number of amendments has been decreasing while the number of new codes of ethics is increasing.
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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".