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Record W3167649636 · doi:10.1177/15562646211023705

A New Era of Indigenous Research: Community-based Indigenous Research Ethics Protocols in Canada

2021· article· en· W3167649636 on OpenAlexafffundabout
Ashley Hayward, Erynne Sjoblom, Stephanie Sinclair, Jaime Cidro

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsFirst Nations Health and Social Secretariat of ManitobaUniversity of WinnipegUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsIndigenousMetisResearch ethicsCommunity-based participatory researchSociologyEngineering ethicsPublic relationsPolitical scienceParticipatory action researchAnthropologyEcology

Abstract

fetched live from OpenAlex

Indigenous communities across Canada have established principles to guide ethical research within their respective communities. Thorough cataloging and description of these would inform university research ethics boards, researchers, and scholars and facilitate meaningful research that respects Indigenous-defined ethical values. A scoping study was conducted of all relevant peer-reviewed literature and public-facing Indigenous research ethical guidelines from First Nations, Metis, and Inuit communities and organizations in Canada. A total of 20 different Indigenous research ethics boards, frameworks, and protocols were identified. Analysis resulted in three key themes: (1) balancing individual and collective rights; (2) upholding culturally-grounded ethical principles; and (3) ensuring community-driven/self-determined research. Findings demonstrate how employment of Indigenous ethical principles in research positively contributes to research outcomes.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Theoretical or conceptuallow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.284
metaresearch head score (Gemma)0.190
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.995

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2840.190
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.014
Science and technology studies0.0330.024
Scholarly communication0.0160.008
Open science0.0070.015
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.797
GPT teacher head0.681
Teacher spread0.116 · 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

Labeled directly by 2 models reading the full record.

MetaresearchScience and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual · Qualitative
DomainMethods
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

Citations125
Published2021
Admission routes3
Has abstractyes

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