‘ <i>They want to get rid of us by all means</i> ’: A critical analysis of policy and governance responses and their implications for street traders’ access to urban space in Harare, Zimbabwe
Bibliographic record
Abstract
This paper examines how urban authorities in Harare respond to street trading and the implications of these interventions on street traders' access to urban space. Drawing from focus groups, in-depth interviews with street traders and urban governance actors, we argue that as urban authorities in Harare become obsessed with defending modernity, they implement aggressive urban policies aimed at eradicating street traders' livelihoods. These violent measures are often differentially experienced with 'street vending mothers', the elderly and those with disabilities bearing the brunt of Harare's authoritarian spatial governance. Alternatively, the city has also experimented with what we call governing through 'spatial containment', aimed at 'taming' street traders and transforming them into formalized entities. We demonstrate that despite its noble intentions, such a policy approach has unintended outcomes for street traders since it undermines the organic attributes of their trade: operational flexibility, spatial mobility and proximity to customers. Despite the negative implications of policy interventions, street traders' associations struggle to champion the collective voice of informal traders due to their organisational fragmentation, unfavourable political environment and existing structural constraints. This study contributes to the broader scholarly debate on urban informality, governance, and socio-economic justice in developing country contexts. Based on our findings, we call for a paradigm shift in policy and governance approaches, advocating for inclusive urban planning, dialogue, and recognition of the socio-economic contributions of street traders.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".