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Record W4415788646 · doi:10.1080/01436597.2025.2566954

Re-thinking the decolonisation of knowledge and dismantling of intellectual imperialism: focusing on epistemic and social justice

2025· article· en· W4415788646 on OpenAlexaff
Caroline M. Schöpf, Justin Felip D. Daduya, Tamari Kitossa, Bandana Purkayastha, Matthew Ming-tak Chew

Bibliographic record

VenueThird World Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsBrock University
FundersUniversity of the Philippines
KeywordsDecolonizationSocial justiceThird worldKnowledge productionEconomic JusticeTransitional justiceGlobal South

Abstract

fetched live from OpenAlex

This introduction discusses the articles in our special issue ‘Decolonising Epistemology’, and derives from them several recommendations for decolonisation: (1) Current decolonisation efforts are mainly led by privileged scholars, when they should be spearheaded by oppressed scholars. Scholars holding privilege should focus on supporting roles. (2) Decolonisation should put dismantling the global power structures erected by colonialism front and centre. (3) Decolonisation should place much greater focus on social justice and activism. (4) Decolonisation should dismantle the global academic power structures that centre the views of the Global North and silence Global South critiques. (5) A focus on epistemic justice may help to highlight intersecting axes of oppression. (6) Is is important to recognise and centre knowledges produced by non-academics. (7) This calls for re-thinking the ways academia currently reviews and evaluates knowledge. (8) There should be a greater focus on how biased, Eurocentric knowledge translates into biased policymaking, which deepens the coloniality of power and facilitates control and exploitation of the Global South. (9) The decolonisation project should place the human and ecological cost of colonisation and neo-colonialism at its centre. Reparations and true social justice for everyone on the planet must be at the heart of decolonisation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.781

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 studies0.0010.001
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 head0.033
GPT teacher head0.321
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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