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Biodiversity Litigation in Canada

2022· book-chapter· en· W4311467147 on OpenAlexaboutno aff
Frédéric Perron-Welch, Chris Tollefson, Joshua Ginsberg

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsDeferenceConvention on Biological DiversityPolitical scienceIndigenousNegotiationGovernment (linguistics)BiodiversityPoliticsInternational lawLawPublic administrationEcology

Abstract

fetched live from OpenAlex

Abstract Although Canada began negotiating and ratifying modern international biodiversity agreements in the 1970s, biodiversity litigation has mostly focused on the implementation of laws and regulations linked to the commitments Canada made when ratifying the Convention on Biological Diversity (CBD). Areas of particular focus have included species at risk, protected areas, and the customary sustainable use of biological resources by Canada’s indigenous peoples. Canada’s courts and tribunals have traditionally shown deference for the actions of other branches of government, but this deference is being challenged where government actions conflict with Canada’s international commitments or with prevailing science. Public interest litigation has shown how courts can grapple with CBD implementation despite traditionalist objections to reliance on international law arguments, and concerns about transforming courtrooms into ‘academies of science’. It remains to be seen whether Canada can reconcile its international commitments to biodiversity and constitutional obligations to its indigenous peoples while it staunchly reaffirms its commitment to resource-based development and the fossil fuel economy. Although this is primarily a political question, litigation offers an avenue to promote change.

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)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.123
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0280.009
Scholarly communication0.0130.003
Open science0.0030.004
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0200.002

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 head0.017
GPT teacher head0.211
Teacher spread0.194 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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