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Record W4281713651 · doi:10.4337/jhre.2022.01.08

Mapping human rights-based climate litigation in Canada

2022· article· en· W4281713651 on OpenAlexaboutno aff
Lisa Benjamin, Sara L. Seck

Bibliographic record

VenueJournal of Human Rights and the Environment · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsAccountabilityPolitical scienceCharterIndigenous rightsPlaintiffInternational human rights lawLawGovernment (linguistics)State (computer science)

Abstract

fetched live from OpenAlex

In line with global trends, there has been an increase in human rights-based climate litigation brought in Canadian courts in recent years. Some litigants invoke human rights as found in the Canadian Charter of Rights and Freedoms to push federal and provincial governments to take seriously the implementation of their climate obligations. Other litigants invoke procedural environmental human rights to engage in free speech and peaceful protest in the face of government action supporting fossil fuel consumption or expansion. At the same time, the Supreme Court of Canada has recognized that Canadian courts could develop civil remedies for corporate violations of customary international law, opening the door to future human rights-based corporate climate accountability litigation. Due to the nascent stage of climate litigation in Canada, this paper maps a broad variety of emerging cases into three interrelated sections: substantive, procedural and corporate accountability litigation. It also highlights emerging and potential future trends, such as the high level of youth and Indigenous plaintiffs. The paper aims to provide a critical overview of emerging Canadian developments in human rights-based climate litigation brought against the state, and reflects on potential strategies for future litigation, including against transnational corporate actors.

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, Insufficient payload (model declined to judge)
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 score0.790
Threshold uncertainty score1.000

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)0.0010.000

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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