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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".