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.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".