Predictors Of Efficiency In Municipal Solid Waste Management In Moses Kotane Municipality In The North-West Province Of South Africa
Bibliographic record
Abstract
A study was conducted in the Moses Kotane Municipality (MKM) in the North-West Province of South Africa in order to identify and quantify factors that affect efficiency in the management of municipal solid waste generated by households and businesses. Data was collected from a combination of 171 households and businesses on 24 socioeconomic, sanitary and environmental indicators of efficiency in the management of municipal solid waste in developing municipalities. The specific objectives of study were to assess the current level of efficiency in the collection and disposal of municipal solid waste, and to construct a framework that could be used for improving the current level of efficiency in the management of municipal solid waste. Efficiency in the management of municipal solid waste was assessed by using ISO 14000 and ISO 14031 standards defined by the Canadian Standards Association. The results showed that about 67% of businesses selected for the study were inefficient in municipal solid waste management, whereas about 33% of them were efficient. Efficiency in the management of municipal solid waste was significantly influenced by 3 predictor variables (lack of adherence to municipal bylaws on waste management, inability to enforce municipal bylaws on municipal solid waste management, and wrong perception on the potential benefits of proper waste management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".