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Record W4309859209 · doi:10.1080/09644016.2022.2146936

Progressive selection and the erosion of Canadian environmental governance: evidence from elite interviews

2022· article· en· W4309859209 on OpenAlexafffundabout
Christopher Orr, James W. Fyles

Bibliographic record

VenueEnvironmental Politics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEnvironmental governanceCorporate governanceStatus quoEliteTransformative learningPoliticsSelection (genetic algorithm)Environmental impact assessmentArticulation (sociology)Environmental studiesGovernment (linguistics)Political scienceEnvironmental ethicsPolitical economySociologyEconomicsLawManagement

Abstract

fetched live from OpenAlex

Canadian environmental governance formally began in the 1970s with an ambitious vision critical of the status quo. Until the Justin Trudeau government from 2015, this vision had been largely eroded when compared to the transformative environmental ideas, ambition, and efforts initially put forth. Drawing on elite interviews in Canadian environmental politics and an articulation of the dominant system, we develop and demonstrate a novel explanation for Canada’s systematic failure to act more ambitiously on the environment. We argue that this failure is the result of progressive selection of Canadian environmental governance in relation to the dominant system at key selection moments. It is at those moments when the environment is least prioritized and the economy most urgently needs attention that environmental governance suffers the most. The paper concludes by emphasizing how features of the dominant political-economic system not typically thought of as environmental can have systematic environmental impacts.

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)0.013
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.011
Science and technology studies0.0300.015
Scholarly communication0.0080.003
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.211
Teacher spread0.166 · 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 designQualitative
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

Citations3
Published2022
Admission routes3
Has abstractyes

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