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Record W2373100111 · doi:10.1177/1049732316649158

“What Do They Really Mean by Partnerships?” Questioning the Unquestionable Good in Ethics Guidelines Promoting Community Engagement in Indigenous Health Research

2016· article· en· W2373100111 on OpenAlexaffabout
Fern Brunger, David Wall

Bibliographic record

VenueQualitative Health Research · 2016
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsGovernment of NunavutMemorial University of Newfoundland
Fundersnot available
KeywordsCommunity engagementIndigenousResearch ethicsOperationalizationPublic relationsSociologyContext (archaeology)AutonomyCorporate governancePolitical scienceEnvironmental ethicsEngineering ethicsLawEpistemology

Abstract

fetched live from OpenAlex

Academics and community members collaborated in research to examine how best to apply ethics guidelines for research involving Indigenous communities in a community with complex and multiple political and cultural jurisdictions. We examined issues of NunatuKavut (Southern Inuit) authority and representation in relation to governance of research in a context where community identity is complex and shifting, and new provincial legislation mandates centralized ethics review. We scrutinize the taken-for-granted assumption of research ethics that community engagement is an unquestionable "good." We examine the question of whether and how research ethics guidelines and associated assumptions about the value of community engagement may be grounded in, and inadvertently reinforce, ongoing colonialist relations of power. We present findings that community engagement-if done uncritically and in service to ethics guidelines rather than in service to ethical research-can itself cause harm by leading to community fatigue, undermining the community's ability to be effectively involved in the research, and restricting the community's ability to have oversight and control over research. We conclude by suggesting that the laudable goal of engaging communities in research requires careful reflection on the appropriate use of resources to operationalize meaningful collaboration.

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)0.215
metaresearch head score (Gemma)0.320
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.968

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2150.320
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0230.128
Scholarly communication0.0270.042
Open science0.0040.026
Research integrity0.0160.021
Insufficient payload (model declined to judge)0.0040.001

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 head0.960
GPT teacher head0.772
Teacher spread0.187 · 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.

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

Citations73
Published2016
Admission routes2
Has abstractyes

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