Bibliographic record
Abstract
Statutory offences that require proof of dishonesty have been enacted in England and Wales since 1968, and similarly, in other jurisdictions (notably, Australia, New Zealand, and Canada). Although dishonesty has been described as an “ordinary concept”, “characterised by recognition rather than by definition”, the concept has proved to be problematic - conceptually and in practice – not least as to whether or not dishonesty is to be judged objectively or subjectively, or involves a hybrid approach (in part objective and in part subjective). For thirty-five years, the approach in England and Wales appeared to be settled following the decision of the Court of Appeal (Criminal Division) in R v Ghosh. However, the UK Supreme Court held in Ivey v Genting Casinos (UK) Ltd - albeit obiter - that when dishonesty is in question the fact-finding tribunal must ascertain (subjectively) the actual state of the individual’s knowledge or belief as to the facts, and thereafter, determine whether his conduct was honest or dishonest by applying (objectively) the standards of “ordinary decent people”. There is no requirement (as stated in Ghosh) that the defendant must appreciate that what he has done is, by those standards, dishonest. This paper considers the history and issues relating to the concept of dishonesty, and it examines the six reasons given by the Supreme Court in Ivey for departing from the decision in Ghosh.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".