Waste Scavenging a Problem or an Opportunity for Integrated Waste Management in Namibia: A Case of Keetmanshoop Municipality, Namibia
Bibliographic record
Abstract
Waste scavenging is an emerging challenge faced by many Municipalities and Local Authorities in Namibia. However, it has been neglected by authorities due to insufficient knowledge about its contribution to resource recovery and recycling. This study investigated how waste scavenging as a problem can be transformed into an opportunity for Integrated Waste Management in Namibia. The main objective of the study was to determine the socio-economic drivers as well as health implications of waste scavenging at Keetmanshoop municipal dumping site, Namibia. Using the purposive sampling method, a total of 45 waste pickers were interviewed through semi-structured questionnaires. The data collected included waste pickers demographic (age, gender, marital status, and level of education), socio-economic impacts (income and diseases) from waste scavenging. The study revealed that the main drivers of waste scavenging are poverty (71.1%) and unemployment (64.4%). Furthermore, waste scavenging contributes significantly to waste pickers’ livelihood through income generation from the sale of waste materials (93.3%). The majority of the waste pickers (80%), scavenge mainly for metals whereas the least target food. The study concluded that waste scavenging, although neglected, contributes significantly to the livelihoods of waste pickers and waste management in Keetmanshoop. The study recommends that waste scavenging should be regulated and integrated into the formal waste management system of the Municipality through avenues such as the formation of the waste picker’s cooperatives that will be registered with the municipality and recognised through formal structures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".