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Record W2776294531 · doi:10.3406/rjenv.2017.7084

L’impossible défi canadien : lutter efficacement contre les changements climatiques, exporter davantage de pétrole, respecter les compétences constitutionnelles des provinces

2017· article· en· W2776294531 on OpenAlexaboutno aff
Jean-Maurice Arbour

Bibliographic record

VenueRevue Juridique de l Environnement · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentLegislaturePolitical scienceConvictionTreatyGovernment (linguistics)Order (exchange)Public administrationMonopolyGreenhouse gasLawBusinessEconomics

Abstract

fetched live from OpenAlex

At the COP 21 in Paris in December 2015, Canada announced its new colors : a 30% reduction in greenhouse gases (GHG) by 2030 compared to 2005. First, we need to look at the broad lines of the Canadian program that was designed to achieve that goal ; it is then necessary to establish a state of affairs in order to better assess the scale of the challenges that arise and the nature of the constitutional problems that are already on the horizon. While the federal government of Canada has the exclusive monopoly to enter into international treaties, this does not mean that the Parliament of Canada necessarily has all the powers required to implement Canada’s international commitments. Indeed, the implementation of the provisions of a treaty in domestic law must follow the rules of the division of legislative powers between the federal order and the provincial order. In the area of climate change and GHG emission control, it appears that the provinces have the essential legislative powers to implement commitments under the Paris Agreement and that their inaction or lack of conviction can seriously compromise the achievement of nationally agreed targets. Lawyers then seek the constitutional basis on which the central Parliament can sit to impose its GHG reduction policies nationwide. There is every reason to believe that it is through criminal regulation only that the central government can achieve its objectives.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.882
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.022
Scholarly communication0.0160.003
Open science0.0020.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0120.001

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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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
Published2017
Admission routes1
Has abstractyes

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