Urban Waste Management in Post-Genocide Rwanda: An Empirical Survey of the City of Kigali
Bibliographic record
Abstract
African countries are urbanizing rapidly, presenting complex challenges for urban waste management. A compelling nexus between urbanization and waste management can be found in Kigali, the capital and largest city in Rwanda. Since its founding in 1907, the city of Kigali has witnessed steady growth in terms of both population and geographical boundaries. Using mainly qualitative methods, this study provides an empirical survey of waste management practices in post-genocide Rwanda with an emphasis on the city of Kigali, the capital. The study analyzed current regulatory arrangements and legislative instruments, approaches to public cleaning, and minimization, collection, and final disposal of wastes. We found Kigali to be an exceptionally clean city with carefully organized and well-coordinated waste management service delivery systems in place. Conversely, the absence of effective minimization strategies, coupled with a lack of safe treatment and disposal facilities, militate against sustainable waste management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".