Identifying predictors of international fisheries conflict
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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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".