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Record W7011634643

Moral self-convictions: uncontested pleas and Canadian criminal law

2023· dissertation· en· W7011634643 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldMedicine
TopicBiomedical and Chemical Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPleaStatutory lawCriminal lawState (computer science)Criminal ConvictionAdversarial system
DOInot available

Abstract

fetched live from OpenAlex

An uncontested plea allows criminal defendants to self-convict without requiring the state to prove its case against them. Uncontested pleas may be inculpatory, exculpatory, or non-inculpatory. Guilty pleas are inculpatory uncontested pleas. When a defendant pleads guilty sincerely, they formally take responsibility for the offence and accept the consequences. Exculpatory and non-inculpatory uncontested pleas include best-interest pleas like \textit{Alford} and nolo contendere pleas, respectively. When a defendant enters one of these pleas, they agree to self-convict without formally taking responsibility for the offence. Statutory language formally forbids exculpatory and non-inculpatory uncontested pleas like nolo contendere pleas in Canada. I argue that the legal and ethical objections to these pleas and plea bargaining generally in Canada are largely misplaced. Nolo contendere pleas open new avenues of plea bargaining for defendants and prosecutors to explore, creating new opportunities for certainty, factual accuracy, agency, and mutual advantage in otherwise highly adversarial proceedings. Although formally forbidden, defendants may still enter nolo contendere informally and surreptitiously. I conclude by arguing that these pleas be formalized and proposing ways to do so.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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