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Record W4400123622 · doi:10.1139/facets-2023-0071

Toward more ethical engagements between Western and Indigenous sciences

2024· article· en· W4400123622 on OpenAlexafffundvenue
Sharon Stein, Cash Ahenakew, Will Valley, Pasang Yangjee Sherpa, Eva Crowson, Tabitha Robin, Wilson Mendes, Steve Evans

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousEurocentrismEnvironmental ethicsColonialismSociologySovereigntyPolitical scienceSocial scienceEpistemologyEngineering ethicsEcologyAnthropologyLawEngineeringBiologyPolitics

Abstract

fetched live from OpenAlex

There is growing interest among Western-trained scientists in engaging with Indigenous sciences. This interest has arisen in response to social pressures to reckon with the colonial foundations of Western science and decentre Western ways of knowing, as well as recognition of the need to draw upon the gifts of multiple knowledge systems to address today's many complex social and ecological challenges. However, colonial patterns and power relations are often reproduced at the interface between Western and Indigenous sciences, including the reproduction of epistemic Eurocentrism and extractive modes of relationship between settlers and Indigenous Peoples. This paper seeks to support Western-trained scientists to recognize and interrupt these patterns in order to create the conditions for more ethical, respectful, and reciprocal engagements with Indigenous sciences. We also offer a map of the different ways that Western sciences have thus far engaged Indigenous sciences. We particularly highlight the emergent possibilities offered by a reparative approach to engagement that emphasizes the responsibility of Western science to enact material and relational repair for historical and ongoing harm, including by supporting Indigenous self-determination and sovereignty in science and beyond.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.660
GPT teacher head0.629
Teacher spread0.031 · 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 designObservational
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

Citations39
Published2024
Admission routes3
Has abstractyes

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