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Record W4308201662 · doi:10.18357/tar131202220790

The River’s Legal Personhood: A Branch Growing on Canada’s Multi-Juridical Living Tree

2022· article· en· W4308201662 on OpenAlexaffvenueabout
Andrew Ambers

Bibliographic record

VenueThe Arbutus Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPersonhoodIndigenousLawCharterIndigenous rightsPolitical scienceLegislationSociologyHuman rightsEcology

Abstract

fetched live from OpenAlex

Relationships with rivers in British Columbia are often imbued with social and material toxicity. Learning from three sources of law in British Columbia—Indigenous, Canadian, and international law—this article draws out one potential remedy to the imbalanced relationships between humans and rivers through exploring the viability of declaring the rights of nature in accordance with the socio-cultural and doctrinal frameworks embedded in these three sources of law. By taking seriously storied precedents and governing practices from the ‘Namgis, Heiltsuk, and W̱SÁNEĆ Nations, this article is guided by their water relations, governance, and legal orders. In expanding Canadian conceptions of personhood, challenging anthropocentrism within section 7 of the Charter of Rights and Freedoms, and expanding section 35 constitutional protections, this article also leverages Canadian legal concepts and protections for remedying river relations. Drawing upon the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) further guides the process of affirming the rights of rivers, especially in light of legislation that has codified UNDRIP domestically. Braiding these three sources of law indicates that subsequent rights of nature cases should be rooted in the interpretative and analytical framework of Canada’s multi-juridical living tree.

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)0.006
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0100.011
Scholarly communication0.0090.003
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.291
Teacher spread0.269 · 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
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

Citations2
Published2022
Admission routes3
Has abstractyes

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