Case Studies
Bibliographic record
Abstract
Details good and unique practices of waste management around the world by reviewing case studies, including (1) a path to zero waste in San Francisco, United States; (2) financial sustainability in Argentina and Colombia; (3) automated waste collection in Israel; (4) cooperation between national and local governments for municipal waste management in Japan; (5) central reforms to stabilize the waste sector and engage the private sector in Senegal; (6) decentralized organic waste management by households in Burkina Faso; (7) successful plastic recycling in Tunisia; (8) extended producer responsibility schemes in Europe; (9) financially resilient bottle recycling in Palau; (10) improved waste collection via an informal sector partnership in Pune, India; (11) improvements in waste management through citizen communication in Toronto, Canada; (12) disaster waste management in Japan; (3) minimizing food loss and waste in Mexico; (14) sustainable source separation in Panaji, India; (15) the use of musical garbage trucks in Taiwan, China; (16) the global tragedy of marine litter; and (17) information management used to reduce waste in Korea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".