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Record W2889996890 · doi:10.1017/s0008423918000410

A Province under Pressure: Climate Change Policy in Alberta

2018· article· en· W2889996890 on OpenAlexaffabout
Brendan Boyd

Bibliographic record

VenueCanadian Journal of Political Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsMacEwan University
Fundersnot available
KeywordsGreenhouse gasClimate changeClimate policyCarbon taxNatural resource economicsPoliticsCarbon leakageLeakage (economics)Environmental policyPolitical scienceBusinessEconomicsEconomic policy

Abstract

fetched live from OpenAlex

Abstract Alberta is responsible for over a third of Canada's greenhouse gas (GHG) emissions. Reducing the country's emissions requires policies and initiatives that reduce emissions in the province. Yet the study of provincial climate change policy in Canada has largely focused on lower-emitting provinces like British Columbia, Quebec and Ontario. This article argues that Alberta is best understood as a “reluctant actor” on climate change, whose policies are influenced by decisions and pressures from outside its borders. The literature on Canadian-American environmental policy making and international policy transfer are used to explore provincial GHG targets and carbon pricing policies. The article finds that Alberta's 2002 targets and Specified Gas Emitters Regulation were determined by economic competitiveness and leakage concerns, while the adoption of new GHG targets in 2008 and a carbon tax was the result of policy transfer through political bandwagoning and the desire for reputational benefits.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.004
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0020.002
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 head0.041
GPT teacher head0.351
Teacher spread0.310 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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