Cricket farming as a livelihood strategy in Thailand
Bibliographic record
Abstract
While many important aspects of wild and farmed insects have been discussed by scholars, such as nutritional value, conservation and farming techniques, no study has addressed how insect farming contributes to rural livelihoods. Furthermore, the roles that interactions between insect farmers, their peers and institutions play in insect farming as a livelihood strategy are even less well understood. This paper presents a preliminary assessment of cricket farming as a livelihood strategy in Thailand. Fortynine cricket farmers participated in in‐depth interviews designed to gain insight into how cricket farming contributes to rural livelihoods. This exploratory study investigates the following research questions: What are the characteristics of Thai cricket farmers and their farms? How do crickets contribute to the lives of rural farmers in Thailand? What role has social and human capital played in cricket farming communities? And what can be learned from the experience of cricket farming in Thailand? Findings suggest that cricket farming has improved the lives of many rural farmers in Thailand not only through the provision of an alternative income source, but through strengthening human and social capital. As such, further empirical data and case study analyses are needed in order to advance our understanding of this particular livelihood strategy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".