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Record W2991191230

Federalism, the Environment and the Charter in Canada

2018· article· en· W2991191230 on OpenAlexaffabout
Dayna Nadine Scott

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsYork University
Fundersnot available
KeywordsSubsidiarityJurisdictionFederalismCharterPolitical scienceEnvironmental lawConstitutionPublic administrationIndigenousEnvironmental justiceLawLaw and economicsEnvironmental planningSociologyBusinessGeographyPoliticsInternational tradeEuropean union
DOInot available

Abstract

fetched live from OpenAlex

This Chapter reviews the key jurisprudential developments in relation to the division of powers in Canada, exploring how the shared jurisdiction over the “environment” created by sections 91 and 92 of the Constitution has historically and continues to shape environmental law and policy. In addition to this federal-provincial struggle, the chapter considers the current trend towards local regulation of environmental matters according to the principle of ‘subsidiarity’, and the growing recognition of the ‘inherent jurisdiction’ of Indigenous peoples. The contemporary dynamics are explored through two critical policy case studies highlighting barriers to environmental justice: safe drinking water on reserves, and climate change mitigation. The review reveals that Canada’s Constitutional framework, while not solely responsible, has contributed to our collective failure to achieve a coordinated and effective set of environmental laws and policies, which translates to unequal distribution of environmental benefits and burdens on the ground. Finally, recent movements to overcome these weaknesses are explored, including recent Charter litigation attempting to define “environmental rights” in Canada, and other attempts to establish a constitutional right to a healthy environment.

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)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.951

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0180.017
Scholarly communication0.0100.003
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.233
Teacher spread0.222 · 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
GenreReview

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
Published2018
Admission routes2
Has abstractyes

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