The waste hierarchy: a strategic, tactical and operational approach for developing countries. the case study of mozambique
Bibliographic record
Abstract
Waste management in Europe is shaped by the waste hierarchy, which guides the legislation and policy of the Member States.This hierarchy applies the following order of priority: (1) prevention; (2) preparing for reuse; (3) recycling; (4) other recovery; (5) disposal.The wide acceptance of this principle in Europe comes from a yearly developed pathway that should not be exported to other countries without consideration of the local framework.Poverty is widespread in most African countries, with the majority of the population surviving on less than 2 US dollars a day and the average waste production per capita being about half the quantity produced in Europe.However, municipal solid waste management is still inadequate throughout the region, with open waste burning and dumping being the only method of disposal in enormous cities.These in turn arise without any planning or basic infrastructure such as roads, sewers or waste landfills.We used the case study of Mozambique to define a strategic, tactical and operational approach for the implementation of the waste hierarchy and to customize it to the local situation.Like other African countries, the major weaknesses in the performance of waste management are the poor legal framework, the lack of institutional structures and the ineffective and poorly coordinated initiatives of the international stakeholders.We propose an interpretation of the waste hierarchy in order to compensate for local deficiencies and to define a local framework for the policy makers whereby prevention is no longer a priority in the short term, in order to stress the awareness and the collection.These are consolidated steps of waste management in industrialized countries but which still pose a challenge in developing nations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".