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Record W3215304170

Judicial Remedies for Climate Change

2021· article· en· W3215304170 on OpenAlexaff
Kent Roach

Bibliographic record

VenueTSpace · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRemedial educationPolitical scienceHuman rightsLawInterimProportionality (law)Declaration
DOInot available

Abstract

fetched live from OpenAlex

The first part of this article outlines remedies granted in climate change litigation directed towards governments in domestic and supra-national courts. It concludes that courts including the German Constitutional Court have tended to grant focused and modest remedies. Attempts to secure more ambitious remedies have generally not been successful. They may have caused North American courts to hold human rights claims based on climate change to be non-justiciable. The second part examines the range of available judicial remedies. It identifies interim relief, the declaration plus, and remedies directed towards laws that violate human rights as more promising remedial strategies. The third part proposes a number of remedial principles. It argues for a two-track remedial approach that combines immediate remedies directed at particular harms with dialogic and interactional remedies in which courts engage with other institutions and parties to produce longer term systemic remedies that will curb emissions in the future. It also suggests that courts should explicitly use proportionality reasoning when factoring in competing social interests and remedial modesty in confronting polycentric problems. The bi-jural remedies that combine human rights and Indigenous law are also promising. Litigants should expect that no one case will remedy the threatening tides of climate change. They should pursue cycles of remedies where new and more intense remedies are used to respond to remedial failures and continued violations of human rights related to global warming.

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: none
Teacher disagreement score0.971
Threshold uncertainty score0.372

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.093
GPT teacher head0.425
Teacher spread0.332 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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