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Record W1931233738 · doi:10.18584/iipj.2015.6.4.2

“I Don't Think that Any Peer Review Committee . . . Would Ever ‘Get’ What I Currently Do”: How Institutional Metrics for Success and Merit Risk Perpetuating the (Re)production of Colonial Relationships in Community-Based Participatory Research Involving Indigenous Peoples in Canada

2015· article· en· W1931233738 on OpenAlexafffundvenueabout
Heather Castleden, Paul Sylvestre, Debbie Martin, Mary McNally

Bibliographic record

VenueInternational Indigenous Policy Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsDalhousie UniversityQueen's University
FundersCanadian Institutes of Health Research
KeywordsIndigenousAccountabilityPrivilege (computing)Participatory action researchColonialismCommunity-based participatory researchSociologyCitizen journalismPublic relationsPolitical scienceLawAnthropology

Abstract

fetched live from OpenAlex

This article reports on findings from a study that explored how a group of leading health researchers who do Indigenous community-engaged research (n = 20) in Canada envision enacting ethically sound research with Indigenous communities, as well as the concomitant tensions associated with doing so. In particular, we explore how institutional metrics for assessing merit and granting tenure are seen to privilege conventional discourses of productivity and validity in research and, as a result, are largely incongruent with the relational values associated with decolonizing research through community-based participatory health research. Our findings reveal that colonial incursion from the academy risk filtering into such research agendas and create a conflict between relational accountability to community partners and academic accountability to one’s discipline and peers.

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)0.029
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.265
GPT teacher head0.436
Teacher spread0.171 · 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

Citations40
Published2015
Admission routes4
Has abstractyes

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