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Record W4402272772 · doi:10.26443/law.v69i1.1370

Mathur v. Ontario

2024· article· en· W4402272772 on OpenAlexaffvenueabout
Stepan Wood

Bibliographic record

VenueMcGill Law Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

In January 2024, the Court of Appeal for Ontario heard an appeal from a lower court’s dismissal of the first Canadian children’s climate case to be decided on the merits. Mathur v. Ontario alleges that Ontario’s climate change legislation, target and plan violate young people’s rights under sections 7 and 15 of the Canadian Charter of Rights and Freedoms by committing the province to dangerously high levels of greenhouse gas (GHG) emissions. This article argues that there are good grounds to allow the appeal. Some favourable findings will likely be upheld, including the court’s acceptance of climate change science, the global carbon budget, global GHG targets, the inadequacy of Ontario’s new target, the disproportionate impacts of climate change on youth and Indigenous peoples, its rejection of a de minimis defence, and its conclusion that the case as a whole is justiciable. There are, however, grounds to reverse the court’s holdings that Ontario’s share of global GHG emission reductions is not justiciable, the alleged harm is not the result of the impugned state action, the claimed right is positive rather than negative, a positive right is not warranted in this case, any deprivation of section 7 rights accords with principles of fundamental justice, and the impugned state action does not constitute age discrimination. The article also addresses some issues left unresolved by the lower court that may prove important on appeal. Whatever happens, the case will set a key precedent for Canadian environmental rights 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 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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0270.003
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0330.003

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 head0.028
GPT teacher head0.305
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2024
Admission routes3
Has abstractyes

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