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Record W4396685741 · doi:10.29173/mlj1215

To What Types of Offences Should the Criminal Code Rules on Organizational Criminal Liability Apply?: A Commenton 9147-0732 Québec Inc c Directeur Des Poursuites Criminelles et Pénales

2020· article· fr· W4396685741 on OpenAlexaboutno aff
Darcy L. MacPherson

Bibliographic record

VenueManitoba Law Journal · 2020
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal codeCriminal liabilityLiabilityCode (set theory)Criminal lawCriminologyPolitical scienceBusinessLawComputer scienceSociologyProgramming languageSet (abstract data type)

Abstract

fetched live from OpenAlex

To What Types of Offences Should the Criminal Code Rules on Organizational Criminal Liability Apply?: A Commenton 9147-0732 Québec Inc c Directeur Des Poursuites Criminelles et Pénales

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)0.028
metaresearch head score (Gemma)0.058
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.153
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.058
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0040.004
Science and technology studies0.0270.012
Scholarly communication0.0140.005
Open science0.0120.006
Research integrity0.1530.075
Insufficient payload (model declined to judge)0.0320.022

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 head0.093
GPT teacher head0.284
Teacher spread0.191 · 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
GenreCommentary

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
Published2020
Admission routes1
Has abstractyes

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