Comparative aspects of cricket farming in Thailand, Cambodia, Lao People's Democratic Republic, Democratic Republic of the Congo and Kenya
Bibliographic record
Abstract
Cricket farming can have a positive impact on rural development and rural economy in low- and middle-income countries. Moreover, crickets have the potential to address food and nutrition insecurity and promote food sovereignty through the promotion of local production and consumption. This paper presents and discusses five complementary studies conducted in Thailand, Cambodia, Lao People's Democratic Republic (Lao PDR), the Democratic Republic of the Congo (DRC) and Kenya. Cricket farming is being promoted in these countries under research projects, public-private partnerships, NGOs and international organisations. In the majority of the countries, cricket farming is still in its infancy and research into how to improve cricket farming systems is still on-going. Cricket farming in Cambodia, Lao PDR, DRC and Kenya remains relatively limited, and many farmers are still a part of pilot projects. In each of the five regions, different cricket species have been a part of traditional diets. As discussed in this paper, many of the potential benefits of the production and consumption of crickets have not yet been realised in many cases due to: (1) lack of adequate support and awareness from stakeholders (especially government agencies); (2) unknown trade volumes; (3) high costs of inputs; and (4) cultural taboos. The information presented in this paper will be especially useful to stakeholders from governmental institutions, non-governmental organisations, civil society organisations and research institutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".