Integrating fisheries and agricultural programs for food security
Bibliographic record
Abstract
Despite the connections between terrestrial and marine/freshwater livelihood strategies that we see in coastal regions across the world, the contribution of wild fisheries and fish farming is seldom considered in analyses of the global food system and is consequently underrepresented in major food security and nutrition policy initiatives. Understanding the degree to which farmers also consume fish, and how fishers also grow crops, would help to inform more resilient food security interventions. By compiling a dataset for 123,730 households across 6781 sampling clusters in 12 highly food-insecure countries, we find that between 10 and 45% of the population relies on fish for a core part of their diet. In four of our sample countries, fish-reliant households are poorer than their counterparts. Five countries show the opposite result, with fish-reliant households having higher household asset wealth. We also find that in all but two countries, fish-reliant households depend on land for farming just as much as do households not reliant on fish. These results highlight the need for food security interventions that combine terrestrial and marine/freshwater programming if we are going to be successful in building a more resilient food system for the world’s most vulnerable people.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".