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Record W2809329947 · doi:10.18584/iipj.2018.9.2.3

“We’ve Been Researched to Death”: Exploring the Research Experiences of Urban Indigenous Peoples in Vancouver, Canada

2018· article· en· W2809329947 on OpenAlexaffvenueabout
Ashley Goodman, Robert Morgan, Ron Kuehlke, Shelda Kastor, Kim Fleming, Jade Boyd, Western Aboriginal Harm Reduction Society

Bibliographic record

VenueInternational Indigenous Policy Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsBritish Columbia Centre on Substance Use
Fundersnot available
KeywordsIndigenousDistrustSociologyTransparency (behavior)Context (archaeology)Neighbourhood (mathematics)Public relationsCommunity engagementCommunity-based participatory researchPovertyPolitical scienceParticipatory action researchGeographyLawAnthropology

Abstract

fetched live from OpenAlex

The belief among many Indigenous Peoples of being over-researched, often through questionable research practices, has generated mistrust towards researchers. Despite growing critiques of conventional research practices, understanding of Indigenous Peoples’ contemporary research experiences remains limited. The research this article describes was undertaken by a community organization led by Indigenous Peoples who use illicit substances. Community researchers facilitated talking circles to explore the research experiences of peers living in a highly-researched inner-city neighbourhood in Vancouver, Canada. While participants reported distrust towards researchers, this wariness did not preclude participation in research given a context of extreme poverty. Participants noted lack of transparency in research and perceived research as having little benefit to their community. We argue for increased support for Indigenous-led approaches to research that emphasize community concerns and meaningful community participation.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0560.020
Scholarly communication0.0080.002
Open science0.0030.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.112
GPT teacher head0.427
Teacher spread0.314 · 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

Citations67
Published2018
Admission routes3
Has abstractyes

Explore more

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