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Record W2768199755 · doi:10.5539/jpl.v10n5p66

Protecting the Environment and People from Climate Change through Climate Change Litigation

2017· article· en· W2768199755 on OpenAlexvenueaboutno aff
Theodore Okonkwo

Bibliographic record

VenueJournal of Politics and Law · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changePolitical economy of climate changeGlobePolitical scienceIntervention (counseling)Climate change mitigationEcological forecastingEnvironmental resource managementEnvironmental planningGeographyEnvironmental sciencePsychologyEcology

Abstract

fetched live from OpenAlex

Climate change litigation seeks to apply legal rights in order to affect the outcomes that would either mitigate, reduce or can even result in improved alternation to climate change. This article intends to identify and analyse the ways through which the environment and the people are protected from rapid changes in climate through the means of climate change litigation. Protection of the environment as well as people by climate change litigation can be witnessed in various nations throughout the globe particularly Australia, the US, Canada and the UK. The research problem examined in this article shows that the courts are becoming a critical climate change front where climate change conflicts are resolved. The research objectives are to help understand how climate change has impacted human health and the environment and how the courts have stepped into the arena to restrain activities that cause climate change impact. The methodology adopted in this article is both doctrinal and theoretical drawing upon primary and secondary sources of information. The key findings and implications to theory and practice of this article is that it is a medium to foster the jurisprudence of the role of climate change regime through judicial intervention in protecting the environment and people from climate change through climate change litigation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.053
GPT teacher head0.315
Teacher spread0.262 · 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.

Study designTheoretical or conceptual
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

Citations7
Published2017
Admission routes2
Has abstractyes

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