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Record W7133847048 · doi:10.1080/13880292.2026.2630504

At Risk or in Jeopardy? Canadian Common Law Species at Risk Jurisprudence

2025· article· en· W7133847048 on OpenAlexaboutno aff
Jordyn Bogetti, Courtney W. Mason

Bibliographic record

VenueJournal of International Wildlife Law & Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsJurisprudenceCommon lawRisk assessmentEnvironmental law

Abstract

fetched live from OpenAlex

As the world’s largest common law country, and the one with the most territory in the critically imperiled Arctic and subarctic regions, Canada is an excellent case study on how common law jurisprudence upholds or undermines the purpose of legislation protecting species at risk. In Canadian legal precedents, there is no leading case on species at risk related matters. This is partially a reflection of the multi-disciplinary nature of environmental legal issues. Depending on the specific facts of each case, a contravention of species at risk laws could be litigated as a criminal, civil, or even an administrative hearing. Because laws exist within and because of social and cultural systems, understanding the socio-cultural reasons why laws were enacted informs how those legal issues are perceived in courts. In this article, we provide a synthesis of the current state of common law species at risk legislation in Canada. We begin by explaining the history and social context of conservation and species at risk legislation development. We then undertake a case law review and analysis for all species at risk related issues, and present the trends in ratios and decisions for three categories of litigation: criminal, administrative, and civil. The results show that while criminal cases tend to uphold species at risk interests, decisions from administrative and civil litigation are less consistent.

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)0.001
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: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.348
Teacher spread0.326 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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