Bridging Weak Links of Solid Waste Management in Informal Settlements
Bibliographic record
Abstract
Many cities in the global South suffer from vast inadequacies and deficiencies in their solid waste management. In the city of Kisumu in Kenya, waste management is fragmented and insufficient with most household waste remaining uncollected. Solid waste enters and leaves public space through an intricate web of connected, mostly informal, actions. This article scrutinizes waste management of informal settlements, based on the case of Kisumu, to identify weak links in waste management chains and find neighborhood responses to bridge these gaps. Systems theory and action net theory support our analysis to understand the actions, actors, and processes associated with waste and its management. We use qualitative data from fieldwork and hands on engagement in waste management in Kisumu. Our main conclusion is that new waste initiatives should build on existing waste management practices already being performed within informal settlements by waste scavengers, waste pickers, waste entrepreneurs, and community-based organizations.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".