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Record W2763919310 · doi:10.15402/esj.v2i1.207

Co-Producing Community and Knowledge: Indigenous Epistemologies of Engaged, Ethical Research in an Urban Context

2017· article· en· W2763919310 on OpenAlexvenueaboutno aff
Heather Howard

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousConceptualizationParticipatory action researchSociologyCommunity-based participatory researchContext (archaeology)Engineering ethicsEnvironmental ethicsCitizen journalismSocial sciencePolitical scienceGeographyAnthropologyEcologyLaw

Abstract

fetched live from OpenAlex

Until recently, the specific and unique ethics considerations of research with the large and diverse populations of Indigenous peoples living in cities have not been adequately addressed. With its emphasis on respect, responsibility, and beneficial outcomes for research participants, community-based participatory research (CBPR) has been described as intrinsically ethical, and in many cases, consistent with a generalized understanding of Indigenous moral values. Through a retrospective reflection on community-engaged research in the urban context of Toronto, this essay examines critically transformations in the conceptualization of ethical research and of CBPR with Indigenous peoples. Historical analysis of urban Indigenous community epistemologies is presented as a dynamic process which informs ethical practice in the production of both community and of knowledge. Community-initiated and implemented research highlights the complexities in urban Indigenous authority-making, complicates contemporary

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)0.017
metaresearch head score (Gemma)0.013
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: none
Teacher disagreement score0.978
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0220.166
Scholarly communication0.0180.009
Open science0.0020.013
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.312
GPT teacher head0.508
Teacher spread0.196 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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Same venueEngaged Scholar Journal Community-Engaged Research Teaching and LearningSame topicIndigenous Health, Education, and RightsFrench-language works237,207