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Record W2125044689 · doi:10.1080/09644016.2013.765686

The comparative politics of courts and climate change

2013· article· en· W2125044689 on OpenAlexaboutno aff
Lisa Vanhala

Bibliographic record

VenueEnvironmental Politics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
FundersBritish AcademyGovernment of the United Kingdom
KeywordsClimate changeDisappointmentPoliticsPolitical economy of climate changePolitical scienceClimate governanceVariety (cybernetics)Corporate governancePolitical economyLawEconomics

Abstract

fetched live from OpenAlex

Disappointment with international efforts to find legal solutions to climate change has led to the emergence of a new generation of climate policy. This includes the emergence of courts as new ‘battlefields in climate fights’. Cross-national comparative analysis of the United Kingdom, Canada and Australia supplements research that has found that litigation plays an important governance gap-filling role in jurisdictions without comprehensive national-level climate change policies. The inductive research design identifies patterns in climate change litigation. The three countries illustrate the varieties of climate policies, and thus serve as a useful entry point for thinking more generally about the interplay between climate politics and legal mobilisation. To improve theoretical understandings of the role of courts in climate change politics, the range of litigants and the variety of cases brought to courts under the umbrella of the term ‘climate change litigation’ are identified.

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)0.008
metaresearch head score (Gemma)0.028
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0110.030
Scholarly communication0.0140.010
Open science0.0010.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0100.000

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 head0.031
GPT teacher head0.294
Teacher spread0.263 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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