Problematizing street vending: The uncanniness of a disembodied urban policy
Bibliographic record
Abstract
Street vending is an important source of livelihood for many urban dwellers in Africa but remains a challenge for urban policy and planning. This paper uses Ghana as a case study to examine how street vending is treated in urban policy frameworks. We use qualitative content analysis to critically review four major urban policies that shape the planning, development, and management of Ghanaian cities. Our findings reveal a misalignment of local urban policies with national policy on the regulation of street vending. The regulation of street vending is unclear, contradictory, and ineffective, failing to provide a clear policy direction and adequate planning tools for integrating street vending into urban spaces. We argue that these policy inconsistencies persist due to power imbalances and governance challenges, with local authorities maintaining ambiguous policies that benefit powerful stakeholders while keeping street vendors in a state of insecurity. We call for more coherent and inclusive policies that recognize the socioeconomic value of street vending and foster more inclusive urban spaces. We also highlight the need for reformed urban governance that empowers marginalized voices and builds cross-scale alliances to address the complexities of urban informality and support the informal economy.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".