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Record W4396715396 · doi:10.29173/mlj1276

The Premier Should Not Also Be the Attorney General: Roncarelli v Duplessis Revisited as a Cautionary Tale in Legal Ethics and Professionalism

2022· article· en· W4396715396 on OpenAlexaboutno aff
Andrew Flavelle Martin

Bibliographic record

VenueManitoba Law Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsLawLegislatureSociologyPolitical science

Abstract

fetched live from OpenAlex

From time to time, a Premier or Prime Minister appoints themself as Attorney General. In this article, I argue that this dual portfolio is inherently and incurably problematic and should be avoided and indeed prohibited. I do so from the perspective of legal ethics and professionalism. The springboard for my analysis is the conduct of Quebec Premier and Attorney General Maurice Duplessis in the classic case of Roncarelli v Duplessis. While there may well be perceived benefits that tempt Premiers to serve in the dual role, any lawyer who does so unavoidably violates his or her professional obligations. For this reason, I argue that law societies or legislatures, or both, should introduce an explicit prohibition against this dual role.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.584
Threshold uncertainty score0.828

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.024
Scholarly communication0.0110.005
Open science0.0030.004
Research integrity0.0190.025
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.381
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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