Traditional Crime vs. Corporate Crime: A Comparative Risk Discourse Analysis
Bibliographic record
Abstract
With the knowledge that risk has become an omnipresent concept used to understand various social problems, this study aims to fill a perceived gap in literature by investigating the way in which risk discourse is applied to understand different categories of crime, namely traditional crime and corporate crime. It is hypothesized that risk logic is heavily applied to the understanding of traditional crime, with minimal attribution to conversations surrounding corporate crime. The pervasiveness of risk as a technique or tactic of government renders the study of its application to different types of crime an important addition to the existing risk literature. Using the method of a comparative content analysis, the parallels and discrepancies between the ways in which risk is used to discuss traditional and corporate crime by Canadian federal criminal justice organizations are explored. The results indicate a lack of focus on risk logic with respect to corporate crime, but demonstrate that risk discourse is perhaps not altogether absent from corporate crime discussions.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".