Designing a ‘vibrant, attractive and sustainable city’: feminist approaches to beautification in Kampala, Uganda
Bibliographic record
Abstract
Beauty, as an aesthetic ideal and intrinsically power-laden paradigm, is central to urban development projects. Yet there remains limited critical work that interrogates the colonial underpinnings, violent outcomes, and negotiations of beauty politics in urban beautification programs. In our article, we approach urban beautification campaigns in downtown Kampala, Uganda via an explicitly African, and Black feminist analytic of beauty. Specifically, we center the experiences of women market vendors as they navigate city greening initiatives and development plans which promise to ‘transform’ Kampala and re-brand it once again as the ‘Garden City of Africa’. We argue that pairing urban beautification and Black and African scholarship around beauty offers generative insights as it understands such spatial programs as always embodied, contested, and inseparable from intersectional power hierarchies. In turn, we take seriously and carefully examine discourses around beautification: by tracing its colonial and gendered foundations and its visceral impacts as it is internalized and renegotiated by low-income women operating in downtown markets in Kampala. As such, our focus on beauty situates beautification as a disciplining and displacing practice and as mentally and physically violent. Finally, it reveals how women try to envision their own beautiful Kampala.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".