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Record W4399438843 · doi:10.1080/0966369x.2024.2352118

Designing a ‘vibrant, attractive and sustainable city’: feminist approaches to beautification in Kampala, Uganda

2024· article· en· W4399438843 on OpenAlexaff
Dominica Whitesell, Caroline Faria, Brenda Boonabaana, Jasper Bakeiha Ankunda, Jovah Katushabe, Phiona Tumuhaise

Bibliographic record

VenueGender Place & Culture · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Sexualities and LGBTQ+ Issues
Canadian institutionsComputer Research Institute of Montréal
FundersNational Science Foundation
KeywordsBeautificationSociologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.150
GPT teacher head0.326
Teacher spread0.176 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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