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Record W2999649168 · doi:10.1108/qrom-04-2019-1754

Indigenous works and two eyed seeing: mapping the case for indigenous-led research

2019· article· en· W2999649168 on OpenAlexaff
Rick Colbourne, Peter W. Moroz, Craig Hall, Kelly Lendsay, Robert B. Anderson

Bibliographic record

VenueQualitative Research in Organizations and Management An International Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ReginaCarleton University
Fundersnot available
KeywordsIndigenousParticipatory action researchOriginalityTraditional knowledgeSociologyCitizen journalismCorporate governanceAction researchDisciplinePublic relationsPolitical scienceEngineering ethicsSocial scienceQualitative researchAnthropologyManagementEngineeringEcologyPedagogyLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore Indigenous Works’ efforts to facilitate Indigenous-led research that is responsive to the socio-economic needs, values and traditions of Indigenous communities. Design/methodology/approach This paper is grounded in an Indigenous research paradigm that is facilitated by Indigenous-led community-based participatory action research (PAR) methodology informed by the Two Row Wampum and Two-Eyed Seeing framework to bridge Indigenous science and knowledge systems with western ones. Findings The findings point to the need for greater focus on how Indigenous and western knowledge may be aligned within the methodological content domain while tackling a wide array of Indigenous research goals that involve non-Indigenous allies. Originality/value This paper addresses the need to develop insights and understandings into how to develop a safe, ethical space for Indigenous-led trans-disciplinary and multi-community collaborative research partnerships that contribute to community self-governance and well-being.

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)0.021
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.146
GPT teacher head0.533
Teacher spread0.387 · 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

Citations45
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueQualitative Research in Organizations and Management An International JournalSame topicIndigenous Health, Education, and RightsFrench-language works237,207