Co-productive Urban Planning: Protecting and Expanding Food Security in Uganda’s Secondary Cities
Bibliographic record
Abstract
Abstract Co-production, a strategy increasingly being adopted by urban planners, has potential for protecting and expanding urban food security. Its goals go beyond those of participation to include substantive sharing in policy design, implementation and monitoring: shifting some power associated with these decisions and actions to primary stakeholders. Co-production is desirable for empowerment outcomes, and also on grounds of greater efficiency, cost savings and more locally informed planning. Slum/Shack Dwellers International (SDI) is a lead actor in co-production and has partnered with the Government of Uganda, working on pro-poor urban development projects underway in several secondary cities, including Jinja and Mbale. SDI frames slum dweller advocacy in a rights-based discourse with provisions that informal settlement residents articulate their own priorities. Given food access is a central priority of the urban poor, co-production creates opportunities to address urban food insecurity. However, governments, including municipal and national governments in Uganda, resist genuine power sharing with urban slum dwellers. This research explores how co-production engages slum dwellers and governance actors in the secondary cities of Jinja and Mbale, Uganda. It seeks to understand the possibilities and limitations of the current SDI co-productive programing in the context of urban food security. Empirical evidence to support this research is drawn from interviews with urban planning stakeholders in Kampala, Jinja and Mbale in 2015 and 2018.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.016 |
| 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".