Code of ethics quality: an international comparison of corporate staff support and regulation in <scp>A</scp>ustralia, <scp>C</scp>anada and the <scp>U</scp>nited <scp>S</scp>tates
Bibliographic record
Abstract
The objective of this paper is to examine the ‘Code of Ethics Quality’ (CEQ) in the largest companies of Australia, Canada and the United States. For this purpose, a proposed CEQ construct has been applied. It appears from the empirical findings that while Australia, Canada and the United States are extremely similar in their economic and social development, there may well be distinct cultural mores and issues that are forming their business ethics practices. A research implication derived from the performed research is that the construct provides a selection of observable and measurable elements in the context of CEQ. The construct of CEQ consists of nine measures divided into two dimensions (i.e. staff support and regulation). They should not be seen as a complete list. On the contrary, it is encouraged that others propose and elaborate revisions and extensions. A practical implication of this paper is a structure of what and how to examine the CEQ in a managerial setting. It may assist companies in their efforts to establish, maintain and improve their ethical culture, norms and beliefs within the organization and supporting them in their ethical business practices with different stakeholders in the marketplace and society. The dimensions and measures of the construct may be used as a frame of reference for further research. They may be useful and applicable across contexts and over time using similar samples when it comes to large companies, as small‐ or medium‐sized ones may not have considered all areas nor have the elements in place. This is a research limitation, but it provides an opportunity for further research.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".