(In)visible fluidities across sandscapes: Sand dredging and local socio-environmental impacts along the Red and Mekong Rivers
Bibliographic record
Abstract
River sand, in its fluid form, is constantly shifting, being displaced through human activities and hydrological processes. The specific connections between the drivers and consequences of sand displacement are difficult to isolate. An emerging concept to help unpack such connections is the sandscape, which encompasses the social-ecological interactions and spatial reconfigurations that the movement and transformation of this granular resource yield. Focusing on riparian communities along the Red River in China and Mekong River in Cambodia, we pay attention to how the fluid interactions of sand and water entwine people, nature and power. We examine how sand mining has emerged alongside other drivers of change in riparian ecosystems, including river damming and infrastructure development. While sand mining impacts local sandscapes – with erosion being the most visible imprint –, riparian communities do not systematically attribute these shifts to sand dredging specifically. Explanations include that people cannot always openly critique sand mining, a sensitive issue in these authoritarian states, but also that sandscape modifications are difficult to parse out amongst the rapid social-ecological changes riparian dwellers experience. We unveil these obscuring factors and enhance sand legibility by further conceptualizing how sand, water, people and power meet and shift across these two sandscapes.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".