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Record W2149444035 · doi:10.18357/ijih.101201513194

Animating the concept of “ethical space”: The Labrador Aboriginal Health Research Committee Ethics Workshop

2014· article· en· W2149444035 on OpenAlexvenueaboutno aff
Fern Brunger, Rebecca Schiff, Melody E. Morton Ninomiya, Julie Bull

Bibliographic record

VenueInternational Journal of Indigenous Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBureaucracyResearch ethicsGovernment (linguistics)Corporate governancePolitical scienceEngineering ethicsProcess (computing)Public relationsSpace (punctuation)Public administrationSociologyManagementEngineeringLawPoliticsEcology

Abstract

fetched live from OpenAlex

This paper reports on an innovative process by which the Inuit and First Nations communities of Newfoundland and Labrador confronted and challenged the policies and procedures of the provincial research ethics system. We describe the ways in which these communities engaged with health and university research review administrators to exchange information, identify challenges with existing processes, and outline a strategy for movement forward. We highlight the innovative structure of the process, and show how that resulted in immediate and ongoing community-led reforms to the provincial research ethics boards. Key to the success of the workshop was the fact that diverse stakeholders—community members, community research review administrators, research ethics board administrators, and health board research administrators—came together in an ethical space and worked together to critically interrogate the bureaucratic structure of the government, health, and university-based ethics review processes in the province. Recommendations arising from this process led to changes in the governance of health research involving the province’s 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

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)0.048
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0120.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.004
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 head0.085
GPT teacher head0.480
Teacher spread0.395 · 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 designNot applicable
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

Citations18
Published2014
Admission routes2
Has abstractyes

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