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Record W2488701813 · doi:10.29173/alr234

Constitutional Authority Over Greenhouse Gas Emissions

2009· article· en· W2488701813 on OpenAlexaffvenueabout
Peter W. Hogg

Bibliographic record

VenueAlberta Law Review · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Immigration
Canadian institutionsYork University
Fundersnot available
KeywordsConstitutionalityStatuteLegislationGreenhouse gasLegislatureGovernment (linguistics)ConstitutionPublic administrationOrder (exchange)Power (physics)BusinessLawPolitical scienceFinance

Abstract

fetched live from OpenAlex

As awareness and concern about global warming increases, Canada’s federal and provincial governments have responded with policies and programs designed to curb greenhouse gas emissions. However, the Constitution of Canada does not specify which level of government has the requisite power to enact the statutes and regulations needed to effectively deal with this pervasive issue. This article explores the constitutionality of a federal program aimed at lowering emissions across the country and concludes that such a program is within the power of the federal government, notwithstanding the fact that it is also within the power of the provincial governments. The author reasons that the emissions reduction program currently being proposed by the federal government is within its legislative power because its complex administrative procedure ultimately culminates in the requisite prohibition and penalty and has a valid criminal purpose. The article concludes by canvassing other possible heads of power under which the federal government could enact such legislation and by exhorting the federal and provincial governments to co-operate in order to stave off a potentially confusing patchwork of overlapping regulations.

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)0.021
metaresearch head score (Gemma)0.022
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 score0.475
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.027
Scholarly communication0.0140.004
Open science0.0030.004
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0070.001

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 head0.034
GPT teacher head0.340
Teacher spread0.306 · 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

Citations10
Published2009
Admission routes3
Has abstractyes

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