Transboundary research in fisheries
Bibliographic record
Abstract
Spatial boundaries have become an indispensable part of regimes and tools for regulating fisheries, with examples including marine protected areas, regional fisheries management organizations and Exclusive Economic Zones. Yet, it is also widely acknowledged that boundaries are a social construct, which may be resisted by both fishers and fish ecology. The ensuing spatial and institutional mismatches have been shown to frustrate management efforts, exacerbating issues of non-compliance and ultimately leading to conflicts and overfishing. Interestingly, the often static and rigid nature of these boundaries has also led to a concomitant research interest in ‘transboundary’. This paradoxical situation of more boundary-setting entailing more transboundary thinking warrants a deeper understanding about boundaries and the role of transboundary research in fisheries. The aims of this review article are twofold: (1) a theoretical clarification on the meanings and uses of spatial boundaries drawing on geographical “boundary studies” literature; and (2) a construction of a typology that outlines how transboundary research is being articulated and envisioned. Together, the study reveals that transboundary scholarship in fisheries are mostly related to resources, fleets, trade and governance aspects and that dealing with the “boundary paradox” encompasses re-incorporating, re-scaling and re-imagining of boundaries. This article provides a conceptual basis for reflecting upon boundaries in world's fisheries and opens up discussions for a more nuanced boundary application that can better cope with multi-level interactions and dynamicity.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".