Bibliographic record
Abstract
Purpose Based on a presentation at the 24th International Symposium on Economic Crime September, 2006, at the University of Cambridge, this paper aims to focus comment on the risk to business generated from organized and economically motivated criminal enterprises to that of risk to reputation. Design/methodology/approach The paper is the considered views of someone who has over 28 years of experience with the Royal Canadian Mounted Police with over 20 of those years conducting white collar crime investigations and/or managing teams of investigators. Findings Law enforcement officers around the globe are being pushed to deliver and, with an increase in public resources, are pursuing corporate criminals with renewed enthusiasm. Shareholders have started choosing their investments based on social responsibility and ethical leadership. Western countries are experiencing the end of the baby boom and employees will soon be in big demand and able to pick where they want to work, thereby reasonably expecting to choose only the most socially responsible companies. This will not include those companies involved in corporate crime! It is asserted that the future does indeed look tougher for those employees, executives or companies who may get involved in corporate crime. Originality/value This paper identifies the risks to business presented by organized and economically motivated criminal enterprises.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".