Participatory action research, strengthening institutional capacity and governance: Confronting the urban challenge in Kampala
Bibliographic record
Abstract
Urban governance presents the most daunting and challenging task for sub-Saharan African countries in this century (Rakodi, 1997: 3; Rakodi, 2001; 5; McGill, 1988; 6). Africa is urbanizing faster than any other region. The level of urbanization stands at 39.1%, with annual rates of growth ranging between 8% and 13%. It is estimated that by 2025 half of the African population will be urban. This demographic shift, particularly in the sub-Saharan region, presents major problems for urban management. Although urban management programs of infrastructure development, financial management, economic development, environmental planning, spatial development mechanisms and social services provision continue to be enhanced, there is a mismatch between the program outcomes and need. Due to this shortfall, alternative strategies have been sought but with little documented evidence of successes, failures and lessons because of limited evaluation. The importance of research-informed policy is underscored by the apparent disconnect between actors in the urban field. These actors include city managers, researchers, political leaders and most important, communities. The latter are often disregarded yet they largely influence the development path and shape the fabric of urban space. Even where communities are engaged, they exert less influence than other actors on urban policies and programs. This paper examines how participatory action research is changing the relationships between researchers, communities and city authorities in a search for alternative approaches to address urban poverty and environmental challenges in Kampala – in particular service delivery, solid waste management and flood control. Based on an action-research and development project conducted in Kampala since 2006, there is evidence that communities can be galvanized not only to design solutions to their problems, but also to engage with city authorities through information sharing platforms about their needs and thus bolster outcomes of urban development programs through improved governance.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| 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".