Exploring the future of fishery conflict through narrative scenarios
Bibliographic record
Abstract
Recent studies suggest that the pervasive impacts on global fishery resources caused by stressors such as overfishing and climate change could dramatically increase the likelihood of fishery conflict. However, existing projections do not consider wider economic, social, or political trends when assessing the likelihood of, and influences on, future conflict trajectories. In this paper, we build four future fishery conflict scenarios by considering multiple fishery conflict drivers derived from an expert workshop, a longitudinal database of international fishery conflict, secondary data on conflict driver trends, and regional expert reviews. The scenarios take place between the years 2030 and 2060 in the North-East Atlantic ("scramble for the Atlantic"), the East China Sea ("the remodeled empire"), the coast of West Africa ("oceanic decolonization"), and the Arctic ("polar renaissance"). The scenarios explore the implications of ongoing trends in conflict-prone regions of the world and function as accessible, science-based communication tools that can help foster anticipatory governance capacity in the pursuit of future ocean security.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".