Moral Panic and White Collar Crime: The Legislative Response
Bibliographic record
Abstract
The price tags attached to white collar crime today are staggering. Clearly losses to public markets such as $70 billion in the US for Enron and $500 million in Canada with Livent are incomprehensible. There are also significant human costs to white collar crime. Victims suffer the loss not only of money but self-esteem and hopes for a fulfilling retirement. Even the casual consumer of the mass media encounters, almost daily, reports of harmful or destructive crime committed by privileged citizens and large corporations. The Canadian government’s response to the moral panic engendered by this emotionally charged news coverage has been the proposal for a mandatory minimum sentence for large frauds and the removal of the availability of a conditional sentence. The scholarship surrounding both these sanctions however is that they do not meet any criminological purpose. Indeed, the empirical evidence is that crime rates generally, and the incidents of fraud specifically have been falling for decades. The perceived risk of economic deceit far outstrips the actual risk.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.024 | 0.029 |
| 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".