Racialised Representations and the Global South: Insights from Critical Race Studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.067 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".