Gender and appropriation of public space in Harare’s contested central city area
Bibliographic record
Abstract
Street trading is a highly gendered form of economic activity practiced by the urban poor in most global south cities. Drawing from focus group discussions and in-depth interviews, this paper examines the everyday struggles facing female street traders with children as they negotiate access to contested urban spaces to make a living in Harare’s Central Business District (CBD). The paper argues that public spaces in Harare’s CBD act as both ‘livelihood spaces’ and places of intense vulnerability for women who have caregiving roles. Female street traders struggle to balance between selling their goods as well as watching for municipal surveillance. These challenges are dire for women with children. The women often engage in precarious strategies to evade municipal enforcement including using their children as ‘shields’ for protection from a highly repressive state machinery that is less sympathetic to the plight of the urban poor. Despite facing these constraints, women enact creative practices to lay claims to urban space. This paper contributes to the ongoing scholarly debates on gender and the informal economy in global south cities. We suggest that urban planning initiatives should be attentive to gendered experiences and needs to create more inclusive and equitable urban environments, where female street traders can engage in their livelihoods without facing harassment or violence.
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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 | 0.015 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".