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Record W2233201293 · doi:10.1177/1556264615599687

Risk and Representation in Research Ethics

2015· article· en· W2233201293 on OpenAlexafffundabout
Fern Brunger, Todd Russell

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsGovernment of NunavutMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsIndigenousIdentity (music)NegotiationResearch ethicsContext (archaeology)PoliticsSociologyRepresentation (politics)Corporate governanceLegislationPolitical sciencePublic relationsPublic administrationLawSocial scienceEngineering ethicsBusiness

Abstract

fetched live from OpenAlex

This article examines Canadian policy governing the ethics of research involving Indigenous communities. Academics and community members collaborated in research to examine how best to apply the Tri-Council Policy Statement guidelines 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 describe the politics of risk--the ways in which collective identity and research risks are co-constructed. Our case study illustrates that collective consent to research must emphasize shifting identity construction in relation to the particular risks and benefits invoked by the research question, to ascertain with which groups or individuals the negotiation of risk should take place in the first place. We conclude by describing a necessary re-imagining of policy governing research ethics involving Indigenous communities.

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
gemmaScience and technology studiesResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
gptMetaresearchScience and technology studiesResearch integrity
Domain: Methods · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.407
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4070.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0170.006
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.039
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 head0.877
GPT teacher head0.735
Teacher spread0.142 · 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.

Science and technology studiesResearch integrityMetaresearch

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

Study designQualitative
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

Citations13
Published2015
Admission routes3
Has abstractyes

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