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Record W2610971724 · doi:10.18584/iipj.2017.8.2.4

Implementing the Tri-Council Policy on Ethical Research Involving Indigenous Peoples in Canada: So, How’s That Going in Mi’kma’ki?

2017· article· en· W2610971724 on OpenAlexafffundvenueabout
Carla Moore, Heather Castleden, Susan Tirone, Debbie Martin

Bibliographic record

VenueInternational Indigenous Policy Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsQueen's UniversityDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDalhousie University
KeywordsIndigenousNova scotiaResearch ethicsPolitical sciencePoliticsPublic administrationQualitative researchSociologyPublic relationsLibrary scienceEngineering ethicsSocial scienceLawEngineeringEthnology

Abstract

fetched live from OpenAlex

The 2010 edition of the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans introduced a new chapter, titled "Research Involving the First Nations, Inuit and Métis Peoples of Canada." The goal of our study was to explore how this chapter is being implemented in research involving Mi’kmaw communities in Nova Scotia. Qualitative data from four groups—health researchers, research ethics board representatives, financial services administrators, and Mi’kmaw community health directors—revealed that while the chapter is useful in navigating this ethical space, there is room for improvement. The challenges they encountered were not insurmountable; with political will from the academy and with guidance from Indigenous community health and research leaders solutions to these barriers can be achieved.

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)0.017
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0350.001
Scholarly communication0.0020.001
Open science0.0030.000
Research integrity0.0000.003
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 head0.148
GPT teacher head0.435
Teacher spread0.287 · 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

Citations22
Published2017
Admission routes4
Has abstractyes

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