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Decolonizing Research Practice: Indigenous Methodologies, Aboriginal Methods, and Knowledge/Knowing

2020· book-chapter· en· W4248480771 on OpenAlexaffabout
Michael D. Evans, Adrian Miller, Peter Hutchinson, Carlene Dingwall

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsIndigenousTraditional knowledgeSociologyWork (physics)Environmental ethicsEngineeringEcology

Abstract

fetched live from OpenAlex

Abstract Indigenous approaches to research are fundamentally rooted in the traditions and knowledge systems of indigenous peoples themselves, although indigenous methodologies and methods have become both systems for generating knowledge and ways of responding to the processes of colonization. Very specific indigenous methods emerge from language, culture, and worldview. This chapter describes two such indigenous research approaches drawn from the work of two indigenous scholars with their communities in Australia and Canada. Although creative and new, these approaches draw deeply from their communities and thus express and enact traditional knowledge systems in contemporary terms. This approach may result in more pertinent research, better take-up and dissemination of research results, and a general improvement in the situations of indigenous communities and peoples.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0110.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.162
GPT teacher head0.449
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations21
Published2020
Admission routes2
Has abstractyes

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