Describing Dishonest Means: The Implications of Seeing 'Dishonesty' as a Course of Conduct or Mental Element and the Parallels with Indecency
Bibliographic record
Abstract
Fundamental differences exist internationally and within over the definition of ‘dishonestly’ and the associated term ‘fraudulently’. In Australia and Canada a further concept of ‘dishonest means’ exists. This article critically examines the Australian High Court’s analysis of ‘dishonest means’ in Peters v The Queen by comparing it with the approach taken by the Canadian Supreme Court in R v. Theroux and R v. Zlatic. The definition of ‘dishonest means’ in Peters is also compared with the exposition of actus reus and mens rea set out in He Kaw Teh v. The Queen, and with similar issues faced by courts in defining acts of indecency. It is argued that in choosing to see ‘dishonest means’ as an element of actus reus, the High Court was mistaken in including the state of mind of the accused as a factor in the characterisation of acts as dishonest. Instead, those mental elements are best placed in the mens rea of an offence. This is because ‘dishonesty’ should be defined as based on either a moral standard or a failure to live up to community expectations. The analysis in Peters conflates these approaches. The complexity generated by Peters suggests that dishonesty is best seen as a purely mental element.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.101 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".