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Record W4409380916 · doi:10.1177/09749284251328227

Racialised Representations and the Global South: Insights from Critical Race Studies

2025· article· en· W4409380916 on OpenAlexaff
James Busumtwi‐Sam, Rina Kashyap

Bibliographic record

VenueIndia Quarterly A Journal of International Affairs · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRace (biology)Gender studiesCritical race theoryPolitical scienceSociologyGeography

Abstract

fetched live from OpenAlex

Explicit representations of race have played a major role in shaping world order since the era of colonialism. Although overt/explicit racisms have retreated in the wake of anti-racism advancements globally, the legacies of historical racial signification continue. Racialised representations have shifted from explicit notions of biological difference to notions of essentialised and primordialised social difference (wherein biological determinism remains implicit), employing seemingly more neutral and acceptable proxies for race, including culture’, ‘ethnicity’ and ‘religion’. Drawing insights from an eclectic body of works loosely termed ‘critical race studies’, we show how ‘racialisation’ as a representational process organises, structures and produces assumptions about race in mainstream Global North (GN) scholarly, policy and influential media representations of the Global South (GS). ‘Racialisation matters’ not because observers in the GN are necessarily racists, but because the legacies of historical racial significations are so deeply embedded structurally and institutionally. ‘Representations matter’ because they continue to inform the lived experiences of people in the GS, producing real physical effects on them as racialised subjects and on the material conditions of their existence. Revelation of the racialised dimensions of representations of the GS is necessary to reclaim the dignity, identity and agency of the racialised.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.357
Teacher spread0.344 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIndia Quarterly A Journal of International AffairsSame topicMigration, Refugees, and IntegrationFrench-language works237,207