Bibliographic record
Abstract
Jordan has seen a sharp increase in municipal solid waste (MSW), partly due to an influx of refugees, with the amount rising from 1.9 million tonnes in 2009 to 2.7 million in 2014. This has placed a significant strain on the country’s waste management systems. Waste pickers play a crucial role in alleviating the burden on municipalities by collecting recyclable materials, yet they remain largely informal, with many earning only US$ 7 per day. Both Jordanian and Syrian refugees collect waste informally, particularly around landfills near refugee camps, such as Al Huseyniyat and Al Akaidir. International cooperation has helped to improve conditions at Jordan’s landfills to address waste management challenges. A 2017 initiative led by the Ministry of Municipal Affairs (MOMA), in partnership with Global Affairs Canada and UNDP Jordan, aims to formalise waste-picking activities, providing official contracts, protective gear, and medical access for workers. The first environmentally friendly sanitary cell was established at the Al-Akaidir landfill in 2018. This project includes provisions to support the economic participation of women in composting facilities. While improvements at landfills are notable, urban waste pickers, particularly Syrian refugees, continue to face challenges. Informal waste pickers in cities remain marginalised, and efforts to formalise and protect their work in these areas are still lacking.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".