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Record W3029292021 · doi:10.18584/iipj.2020.11.2.10635

A Principled Approach to Research Conducted with Inuit, Métis, and First Nations People: Promoting Engagement Inspired by the CIHR Guidelines for Health Research Involving Aboriginal People (2007-2010)

2020· article· en· W3029292021 on OpenAlexafffundvenueabout
Janet Jull, Alexandra King, Malcolm King, Ian D. Graham, Melody E. Morton Ninomiya, Kristen Jacklin, Penny Moody-Corbett, Julia E. Moore

Bibliographic record

VenueInternational Indigenous Policy Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of OttawaNOSM UniversityUniversity of SaskatchewanWilfrid Laurier UniversityQueen's University
FundersCanadian Institutes of Health Research
KeywordsIndigenousContext (archaeology)CommissionPublic relationsPolitical scienceSociologyLawGeography

Abstract

fetched live from OpenAlex

Research to address the health burdens experienced by Indigenous populations is essential. In the Canadian context, the Truth and Reconciliation Commission of Canada determined that these health burdens are the result of policies that have undermined opportunities to address community-level health needs. The Canadian Institutes of Health Research Guidelines for Health Research Involving Aboriginal People (2007-2010), or“CIHR Guidelines,” were prepared in a national consultation process involving Inuit, Métis, and First Nations communities, researchers, and institutions. This paper asserts that the principles espoused in the CIHR Guidelines hold ongoing potential to guide health research with Indigenous people in ways that promote equitable research partnerships. We encourage those in research environments to engage with the spirit and content of the CIHR Guidelines.

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)0.015
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0370.000
Scholarly communication0.0010.000
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 head0.223
GPT teacher head0.494
Teacher spread0.271 · 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 designQualitative
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

Citations23
Published2020
Admission routes4
Has abstractyes

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