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Record W2888264608 · doi:10.5204/ijcis.v11i1.557

Canadian and Australian First Nations: Decolonising knowledge

2018· article· en· W2888264608 on OpenAlexaboutno aff
Josie Arnold

Bibliographic record

VenueInternational Journal of Critical Indigenous Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipDecolonizationIndigenousPostcolonialism (international relations)AlienationContext (archaeology)IdeologySociologyTraditional knowledgeEpistemologyPolitical scienceSocial scienceLawHistoryArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

This article explores Indigenous standpoint theory in Australia in the context of postcolonialism and some of its aspects influencing Canadian First Nations scholarship. I look at how cultural metanarratives are ideologically informed and act to lock out of scholarship other ways of knowing, being and doing. I argue that they influence knowledge and education so as to ratify Eurowestern dominant knowledge constructs. I develop insights into redressing this imbalance through advocating two-way learning processes for border crossing between Indigenous axiologies, ontologies and epistemologies, and dominant Western ones. In doing so, I note that decolonisation of knowledge sits alongside decolonisation itself but has been a very slow process in the academy. I also note that this does not mean that decolonisation of knowledge is always necessarily an oppositional process in scholarship, proposing that practice-led research (PLR) provides one model for credentialling Indigenous practitioner-knowledge within scholarship. The article reiterates the position of alienation in their own lands that such colonisation implements again and in an influential and ongoing way. The article further proposes that a PhD by artefact and exegesis based on PLR is potentially an inclusive model for First Nations People to enter into non-traditional research within the academy.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.429
Teacher spread0.381 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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