Precarious livelihoods at the intersection of fishing and sand mining in Cambodia
Bibliographic record
Abstract
Fishing and sand mining in Cambodia may not appear to have much in common. However, digging deeper reveals important parallels. Both fishing and sand mining support livelihoods and are connected to a limited natural resource. Meanwhile, they are both typified by precarious livelihoods, on the one hand, and overexploitation, on the other. In bringing these two topics together, the paper combines empirical qualitative research from two separate studies conducted by the co-authors in Cambodia, one in coastal fishing villages and another in the sand mining industry along the Mekong River. We argue that the interplay between fishing and sand mining has paradoxical impacts on livelihoods, supporting one group while undermining another. Using a precarity analysis lens, we show how an unconventional, and largely invisible frontier of natural resource exploitation-sand mining-is intertwined with fisheries, and expands our understanding of the relationship between precarious labour, environmental change, and livelihoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".