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Record W2111082066 · doi:10.1177/1077800411409885

In Indigenous Words: Exploring Vignettes as a Narrative Strategy for Presenting the Research Voices of Aboriginal Community Members

2011· article· en· W2111082066 on OpenAlexafffundabout
Amy T. Blodgett, Robert J. Schinke, Brett Smith, Duke Peltier, Chris Pheasant

Bibliographic record

VenueQualitative Inquiry · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsFirst Nations Health and Social Secretariat of ManitobaLaurentian University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsParticipatory action researchPraxisIndigenousMainstreamSociologyNarrativeContext (archaeology)Action researchCitizen journalismGender studiesPedagogyPolitical scienceAnthropologyHistory

Abstract

fetched live from OpenAlex

Recently, awareness within academia has grown regarding the incompatibilities of mainstream research with indigenous cultures as well as the historical injustices that have accrued through colonizing practices. Accordingly, support for alternative (non-Westernized) research approaches has been increasing. Participatory action research (PAR) and cultural praxis reflect two approaches where researchers advocate for a movement toward cultural inclusivity. Both approaches have been integrated within the current work amongst mainstream academics and Aboriginal community members in Northeastern Ontario, Canada. The purpose of the current project was to empower Aboriginal coresearchers to share their voices regarding research, grounded within their lived experiences and the surrounding cultural context. Vignettes were developed as a method for presenting each Aboriginal coresearcher’s story in their “own words.” In this article, vignettes are explored as a potential method for centralizing indigenous voices and ultimately enabling PAR and praxis.

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)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.006
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.454
GPT teacher head0.546
Teacher spread0.093 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations173
Published2011
Admission routes3
Has abstractyes

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