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Record W4405297282 · doi:10.1080/17441692.2024.2436436

Indigenous sovereignty in research and epistemic justice: Truth telling through research

2024· article· en· W4405297282 on OpenAlexaff
Raglan Maddox, Melody E. Morton Ninomiya

Bibliographic record

VenueGlobal Public Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSovereigntyIndigenousEconomic JusticePolitical scienceSociologyEnvironmental justiceEpistemologyEthnologyLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

in which Indigenous knowledge systems are recognised and valued in research-related contexts. We draw attention to how colonial knowledge systems silence, delegitimise and devalue specific knowers and ways of knowing, being and doing - through truth telling. This includes (1) the extent to which educational systems, research, practices, decisions, and reported outcomes are whitewashed - a process of structural and systemic discrimination, racism, and exclusion that actively alters or omits Indigenous and non-Euro-Western contributions and perspectives to fit Euro-Western norms and (2) whitewashed and racialised logic in scientific research that claims to be open, collaborative and transparent. Whitewashing not only obscures the history and contributions of Indigenous peoples and communities but also actively reinforces systemic biases and inequities. We assert the need for epistemic justice in public health research. Epistemic justice calls for Indigenous sovereignty and self-determination to be made visible. It may involve on how colonial policies, protocols, and regulations are connected to everyday lived inequities of Indigenous communities, families and individuals. Ultimately, epistemic justice is inherent to Indigenous peoples' health and wellness, self-determination and sovereignty.

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)0.029
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0110.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.315
GPT teacher head0.530
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations14
Published2024
Admission routes1
Has abstractyes

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