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Record W2918728312

Making Dishonesty Fit the Crime

2018· article· en· W2918728312 on OpenAlexaboutno aff
Rudi Fortson

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsDishonestySupreme courtAppealLawStatutory lawTribunalPolitical sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.368
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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