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Record W6981934961

Gerard V. La Forest and the Uncertain Greening of Canadian Public Law

2013· article· en· W6981934961 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2013
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationConstitutionalityGovernment (linguistics)Environmental lawParliamentResponsible governmentDumpingFederal lawPublic participation
DOInot available

Abstract

fetched live from OpenAlex

This tribute essay concentrates on Justice La Forest's contributions to Canadian public law in three leading environmental cases. The first was R. v. Crown Zellerbach Canada Ltd.,' where the issue was the constitutionality of federal legislation on marine pollution that applied to the dumping of material into provincial waters by a provincial undertaking. While agreeing with his colleagues that marine pollution was a matter on which the federal government could legislate, La Forest J. wrote a provocative dissent to the ruling of the court that marine pollution came within Parliament's residual authority to legislate on peace, order and good government (POGG). The second case was Friends of the Oldinan River Society v. Canada (Minister of Transport) in which La Forest J. wrote the opinion for a nearly unanimous court. It upheld the authority of the federal government to require federal departments and agencies to conduct environmental assessments before exercising regulatory powers or committing federal spending in relation to provincial projects. The third case was R. v. HydroQuebec, in which a razor-thin majority agreed with La Forest J. that the federal power over criminal law authorized the Canadian Environmental Protection Act, 19994 (CEPA), which created a broad regulatory framework for the manufacture, importation, sale, use and disposal of toxic substances.

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)0.007
metaresearch head score (Gemma)0.018
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: Other · Consensus signal: Other
Teacher disagreement score0.147
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0450.020
Scholarly communication0.0130.004
Open science0.0040.004
Research integrity0.0160.012
Insufficient payload (model declined to judge)0.0090.001

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 head0.051
GPT teacher head0.349
Teacher spread0.298 · 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
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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