Integrating fisheries and agricultural programs for food security
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it