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Record W3005454208 · doi:10.1163/15718115-02702012

Consent, Resistance and the Duty to Consult

2020· article· en· W3005454208 on OpenAlexaffabout
Avigail Eisenberg

Bibliographic record

VenueInternational Journal on Minority and Group Rights · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousResistance (ecology)DutyState (computer science)Agency (philosophy)Political scienceInformed consentGovernment (linguistics)PoliticsLawIndigenous rightsStatement (logic)Public relationsPublic administrationBusinessSociologySocial scienceMedicine

Abstract

fetched live from OpenAlex

Until recently, conflicts between Indigenous peoples and the Canadian state over land development projects have proceeded without the requirement that the state or companies obtain Indigenous consent. In 2018, this changed when the Government of Canada released a statement identifying ‘free, prior, and informed consent’ (fpic) as a requirement of meaningful engagement on projects that implicate Indigenous rights. This article considers the promise of consent within consultation processes. Consent is better than its absence, but conflicts over land development often involve rival claims to authority. The principle of consent cannot alone address the challenges posed by these rival claims nor offer appropriate responses to them. Through organised resistance, communities develop collective agency, forge political alliances, and re-appropriate their authority over territory and resources that are significant to them. The introduction of fpic clarifies but does not replace the benefits of resistance for some communities.

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)0.112
metaresearch head score (Gemma)0.149
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 score0.112
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1120.149
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0200.094
Scholarly communication0.0130.014
Open science0.0040.018
Research integrity0.0230.019
Insufficient payload (model declined to judge)0.0090.002

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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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

Citations30
Published2020
Admission routes2
Has abstractyes

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