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Record W2981541399 · doi:10.18584/iipj.2019.10.4.8372

Towards an Indigenous-Informed Relational Approach to Free, Prior, and Informed Consent (FPIC)

2019· article· en· W2981541399 on OpenAlexaffvenueabout
Terry Mitchell, Courtney Arseneau, Darren Thomas, Peggy Smith

Bibliographic record

VenueInternational Indigenous Policy Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsLakehead UniversityWilfrid Laurier University
Fundersnot available
KeywordsIndigenousProsperityIndigenous rightsPolitical scienceInformed consentGeneral partnershipPoliticsPublic administrationSociologyPublic relationsLawMedicine

Abstract

fetched live from OpenAlex

International and domestic rights frameworks are setting the stage for the full recognition of Indigenous Peoples’ rights in Canada. However, current political promises to restore Indigenous relations, to reconcile historic wrongs, and to foster mutual prosperity and well-being for all people within Canada remain woefully unfulfilled. Indigenous Peoples continue to call for full engagement with emerging Indigenous rights frameworks such as the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) and its principles of free, prior, and informed consent (FPIC). This article discusses the key findings from a multi-year university–community research partnership with Matawa First Nations in which we collaboratively seek to advance understanding of consultation processes and Indigenous experiences of and perspectives on FPIC. The article, based on several years of dialogue and interviews and a two-day workshop on FPIC, offers insight into Indigenous perspectives on FPIC advancing an Indigenous-informed relational approach to consultation and consent seeking.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.372
Teacher spread0.336 · 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.

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

Citations27
Published2019
Admission routes3
Has abstractyes

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