Identifying predictors of international fisheries conflict
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
Abstract Marine capture fishery resources are declining, and demand for them is rising. These trends are suspected to incite conflict, but their effects have not been quantitatively examined. We applied a multi‐model ensemble approach to a global database of international fishery conflicts between 1974 and 2016 to test the supply‐induced scarcity hypothesis (diminishing supplies of fishery resources increase fisheries conflict), the demand‐induced scarcity hypothesis (rising demand for fishery resources increases fisheries conflict), and three alternative political and economic hypotheses. While no single indicator was able to fully explain international conflict over fishery resources, we found a positive relationship between increased conflict over fishery resources and higher levels of per capita GDP for the period 1975–1996. For the period 1997–2016, we found evidence supporting the demand‐induced scarcity hypothesis, and the notion that an increase in supply of fishery resources is linked to an increase in conflict occurrence. By identifying significant predictors of international fisheries conflict, our analysis provides useful information for policy approaches for conflict anticipation and management.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it