Working Paper 49: Dirty deals – Case studies on corruption in waste management and trade
Bibliographic record
Abstract
Waste management is a huge industry at the local, national and international levels. Public services play a key role in dealing especially with waste generated by households. Getting waste management right is essential if we are to achieve a circular economy and the Sustainable Development Goals. Complex legal frameworks and their weak implementation open up spaces for criminals to profit from illegally managing or trading in waste. The consequences on the environment and human health can be severe. The role of corruption in crimes involving waste is unexplored. An initial analysis shows the potential for corruption to play a role in: influencing policy decisions involving waste management; corrupt deals involving the selection of waste management companies linked to powerful elites; schemes to gain lucrative waste management contracts through systematic bribery; illegal imports of hazardous substances for profit, avoiding or suppressing formal controls. The report explores five case studies (Albania, Lebanon, North Macedonia, Canada–Philippines, US–South America) and proposes a typology of corruption patterns in crimes involving waste management and trade.
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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 | 0.000 | 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 | 0.000 |
| 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".