Evidence of spatial competition, over resource scarcity, as a primary driver of conflicts between small-scale and industrial fishers
Bibliographic record
Abstract
Accounts of fishing conflicts have been rising globally, particularly between small-scale and industrial vessels.These conflicts involve verbal or physical altercations, and may include destruction of boats, assault, kidnapping, and murder.Current scholarship around industrial/small-scale fishing conflicts theorizes them as a form of resource conflict, where fish scarcity is the dominant contributor to conflict and competition.Alternatively, conflicts may be driven by spatial competition, concentrating where there are increased encounters, unrelated to resource status.Current policies to address these conflicts focus on enforcing the separation of small-scale and industrial vessels; however, this broad spatial separation has yet to be evaluated for deterring conflicts.Here we employ a novel spatial analysis to estimate the locations of industrial/small-scale conflicts at sea in Ghana, West Africa.Using data from narrative reports over the period of 1985 to 2014, we combine qualitative information on depth and shoreline indicators to analyze conflict locations.We find virtually all expected conflict locations (98%) occurred within the zone meant to exclude industrial vessels, and conflicts concentrated primarily around major ports.Our results suggest conflicts are likely more related to spatial patterns of vessel presence than patterns of resource use.These findings suggest a critical need for evidence-based and contextual information on the drivers of fisheries conflicts, rather than continued reliance on assumptions of resource scarcity.They also suggest that nuanced policies that reduce vessel encounter and clarify exclusive spatial rights may be more important in responding to these conflicts than approaches designed to broadly separate fleets or increase fish stocks.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".