Sand ecologies, livelihoods and governance in Asia: A systematic scoping review
Bibliographic record
Abstract
Sand, gravel, and crushed rock – known as construction aggregates – are in high demand in the Asian region. Such demand is driven by high rates of urbanization, infrastructure development, and dam building: an unprecedented amount of sand is being extracted from the region's river, delta and estuary areas, only to be transported for infill or construction purposes elsewhere. This systematic scoping review examines the state of knowledge in the peer review literature on sand ecologies, livelihoods, and governance in Asia. We find that the literature mainly focuses on the ecological implications of sand mining, namely biotic and abiotic components: sand mining is linked with many forms of ecological degradation, although partial ecosystemic recovery may be possible when sand mining stops. In contrast, the limited analysis on livelihoods suggests that violence, work-related injuries, and precarious jobs are common for those working in the sand industry, with sand mining producing different types of work depending on the level of mechanization. We conclude by noting several gaps in the literature, including the narrow geographical focus (mainly India and China), the lack of attention to the intersection between sand mining and other anthropogenic disturbances, and the need to establish transparent sand governance processes within this region.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".