Bibliographic record
Abstract
Increasingly plastic pollution is being recognized as a critical environmental and human health threat of unprecedented scale and complexity. While trends in plastic production and consumption are still on the rise, the negative effects of uncollected, mismanaged, dumped or incinerated plastic waste are causing profound impacts on the environment, oceans, climate and food chains compromising the quality of life for humans and other living beings, with expected cumulative negative effects for the near future. Particularly populations in the Global South, where new markets for plastic consumer goods have rapidly emerged over the past 30 years, while waste management, in general, has remained precarious, underfunded or inexistant, directly experience the burdens of plastic pollution. The emerging environmental problems are particularly visible in these regions and so are also possible solutions and alternatives. Approximately 20 million informal workers already recover plastic waste from the garbage in the Global South, usually working under precarious, risky and poorly paid conditions. The literature claims that they represent a work force that if recognized, integrated and valued and under decent work conditions could potentially increase significantly the capturing of plastic waste and reduce the amount of fugitive plastics. This review paper applies an anthropogenic global environmental change theory lens to discusses the key challenges in managing plastic waste and global plastic pollution, uncovering major causes, impacts from dispersion and leakage of plastics into soil, water and air, recognizing the relational and geographic perspectives of plastic waste. A concerted effort is required in coordinating policies and technological solutions in order to strengthening, fund and recognize the waste picker sector as key protagonist in addressing this waste issue.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".