Fishing Livelihoods and Diversifications in the Mekong River Basin in the Context of the Pak Mun Dam, Thailand
Bibliographic record
Abstract
Fishing livelihoods are under stress in many regions of the world, including the lower Mekong river basin. Building on research on the socio-economic impacts of hydroelectric development, this paper explores the spatial dimensions of livelihood diversifications. Research in 2016 and 2017, involving 26 semi-structured interviews in nine upstream, downstream, tributary and relocated villages in the vicinity of the Pak Mun hydroelectric dam, provides insight into how villagers have coped and adapted fishing livelihoods over time. Results are consistent with other research that has detailed the adverse effects of hydroelectric development on fishing livelihoods. Interviewees in the nine communities in the Isan region of Thailand experienced declines in the abundance and diversity of fish valued as food, and engaged in other household economic activities to support their families, including rice farming, marketing of fishing assets and other innovations. Stories of youth leaving communities (rural-urban migration) in search of employment and education were also shared. Although exploratory, our work confronts theories that fishing is a livelihood practice of “last resort”. Narratives suggest that both fishing and diversification to other activities have been both necessary and a choice among villagers with the ultimate aim of offsetting the adverse impacts and associated insecurity created by the dam development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".