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Record W2998362432 · doi:10.24908/fede.v21i1.13849

How Does Canada’s Decentralized Federation Impact Environmental Regulations

2019· article· en· W2998362432 on OpenAlexaffvenueabout
Reeba Khan

Bibliographic record

VenueFederalism-E · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAmbiguityRebuttalGovernment (linguistics)FederalismArgument (complex analysis)ConstitutionPolitical sciencePublic administrationEnvironmental lawEnvironmental policyLaw and economicsLawEconomicsEnvironmental resource managementPolitics

Abstract

fetched live from OpenAlex

This paper argues Canada’s decentralized federation plays an influential role in thwarting environmental regulations at the federal and provincial levels of government. The text is structured as follows: the author’s argument comes first, which is followed by an opposing viewpoint and a subsequent rebuttal reaffirming the author’s stance. The paper’s evidence is based on scholarly journal articles, lectures, and textbooks. The author’s arguments build on the Constitution’s ambiguity regarding environmental responsibilities: the ambiguity promotes a piecemeal approach to environmental regulations at the provincial level. This results in unsynchronized policies across Canada which weakens the overall efficacy of the environmental policies. Moreover, despite attempts at clarifying environmental responsibilities there is still dissonance because Canada is a decentralized federation. Subsequently, the federal government cannot force synchronization or compliance. Furthermore, at times, the federal government must underplay its hand even when it takes constitutionally charged actions because sound environmental actions require provincial actions as well. Ultimately, this paper showcases how decentralized federalism plays federal and provincial level governments against each other when it comes to environmental regulations. This results in poor environmental policies at all levels of government.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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 teacher head, 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
Published2019
Admission routes3
Has abstractyes

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