Sentencing the corporate offender: Legal and social issues
Bibliographic record
Abstract
Introduction - so what does 'and society' mean?, Cyrus Tata. Part 1 The International Movement Towards Transparency and 'Truth in Sentencing': Getting tough on crime - the history and political context of sentencing reform developments leading to the passage of the crime act, Judith Greene; A sentencing matrix for Western Australia - accountability and transparency or smoke and mirrors?, Neil Morgan; Mandatory sentences - a conundrum for the new South Africa?, Dirk van Zyl Smit; Are guided sentencing and sentence bargaining incompatible? - perspectives of reform in the Italian legal system, Grazia Mannozzi; Legislation and practice of sentencing in China, Liling Yue; Sentencing reform in Canada - who cares about corrections?, Mary E. Campbell. Part 2 The Truth About Public and Victim Punitiveness - What do we Know and What do we Need to Know?: Public knowledge and public opinion of sentencing, Mike Hough and Julian V. Roberts; Crisis and contradictions in a state sentencing structure, B. Keith Crew, Gene Lutz and Kristine Fahrney; Harsher is not necessarily better - victim satisfaction with sentences imposed under a 'truth in sentencing' law, Candice McCoy and Patrick McManimon Jr. Part 3 Measuring Punishment - Conceptual and Practical Problems and Resolutions: European sentencing traditions - accepting divergence or aiming for convergence?, Andrew Ashworth; What's it worth? - a cross-jurisdictional comparison of sentence severity, Arie Frieberg; Sentencing burglars in England and Finland - a pilot study, Malcolm Davies, Jukka-Pekka Takala and Jane Tyrer; A new look at sentence severity, Brian J. Ostrom and Charles W. Ostrom; Desert and the punitiveness of imprisonment, Gavin Dingwall and Christopher Harding; The science of sentencing - measuring theory and von Hirsch's new scales of justice, Julia Davis; Scaling punishments - a reply to Julia Davis, Andrew von Hirsch; Scaling punishments - a response to von Hirsch, Julia Davis. Part 4 Reason
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".