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Record W2909047292 · doi:10.1111/ropr.12320

Autonomous Local Climate Change Policy: An Analysis of the Effect of Intergovernmental Relations Among Subnational Governments

2019· article· en· W2909047292 on OpenAlexafffundabout
Elizabeth Schwartz

Bibliographic record

VenueReview of Policy Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsUniversity of Saskatchewan
FundersPacific Institute for Climate Solutions
KeywordsClimate changeLocal governmentCorporate governanceClimate governanceAutonomyIntervention (counseling)Government (linguistics)Climate change mitigationPolitical sciencePolitical economy of climate changeGreenhouse gasPublic administrationBusiness

Abstract

fetched live from OpenAlex

Abstract Local governments have emerged as important players in climate change governance, both at home and on the international stage. Likewise, action by states and provinces has been increasingly highlighted, particularly as national actors have moved slowly to reduce greenhouse gas emissions. But to what extent do local governments act independently from state and provincial governments in the area of climate change mitigation? Using an explicit process tracing approach, the article tests two hypotheses regarding the influence of upper level subnational governments on local policy. In Vancouver, British Columbia, Canada, a city that is a climate change leader, provincial government intervention cannot explain the results of climate change mitigation policy making. This suggests that local governments can exercise an important degree of autonomy over climate change policy, but also implies that where municipalities are less independently committed to climate action, active upper level government intervention will likely be needed.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.048
GPT teacher head0.457
Teacher spread0.409 · 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 designObservational
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

Citations20
Published2019
Admission routes3
Has abstractyes

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