6 - She is so Pretty, Look at her Hair’: Perspectives on the Racialisation of Mixed- Race Persons in Ghana
Bibliographic record
Abstract
This article interrogates the processes of the racialisation of hair and skin colour of ‘mixed-race’ Ghanaians in the specific context of Ghana. The article draws on the findings of a larger qualitative multisite study that explored the lived experiences and identity construction of Ghanaian mixed-race persons living in Ghana and Canada, Informed by race and racialisation theories, the article argues that hair politics cannot be discussed without considering how skin colour and gender also contribute to shaping the racial categorisation of mixed-race individuals in Ghana. The authors show how Ghanaian society ‘others’ and racialises mixed-race Ghanaians (for example, through school regulations on hair and hair style); how mixed-race individuals navigate the racial gaze, discourses and practices around their body; how they reproduce and challenge racialised considerations and imaginaries; and how they perform their own racial identities through their hair. The article links these narratives with the ideals of white racial hegemony that permeate society. Acknowledging mixed-race Ghanaians’ experiences of privilege, the authors argue that the valuation of their whiteness by the majority population is not straightforward and does not lead mixed-race individuals to self-identify as whites. Mixed-race Ghanaians, rather, have fluid experiences i terms of privileges and discrimination based on context and interactions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".