Bibliographic record
Abstract
In recent years, corporate criminal liability has featured as a prominent item on the agenda for law reformers internationally, the dissatisfaction with the identification doctrine as traditionally applied within Commonwealth jurisdictions being starkly illustrated by proposals and legislative responses involving deliberate and sharp breaks from the structure of corporate liability fashioned by the courts. Having regard to the common law's unfalteringly 'nominalist' approach to corporate criminal liability, that is, an approach which treats the corporation as 'nothing more than a collection of individuals', and the articulated resistance to moving radically beyond identification theory as a means of grounding liability in cases of serious crime, this paper examines whether New Zealand companies10 ought to be made subject to a more extensive and clearly defined criminal liability regime. In light of the recent legislative developments in Australia, England and Wales, and Canada, the question of whether introduction of a separate corporate homicide offence is desirable in New Zealand is explored as a secondary issue.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| 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".