Bibliographic record
Abstract
The number of fishing vessels in Pakistan's coastal waters has been steadily increasing but the fish catches steadily decreasing. Press reports blame this on poverty-driven overfishing by the artisanal fishers with banned nets. Using qualitative methods and tapping into local stakeholder knowledge, this study dispels simplistic views by exploring in depth the coastal artisanal fisheries sector in Pakistan and documenting the challenges faced by it. We argue that the poverty–resource degradation nexus is mediated through a poverty–credit market nexus and that addressing this problem will resolve imperfections in both the credit and product markets. Further, we argue that fishery degradation also has other causes and that addressing these and empowering communities will ease the poverty–resource degradation nexus. Résumé Le nombre de navires de pêche dans les eaux côtiéres du Pakistan n'a cessé d'augmenter, alors que les captures de poissons sont en baisse constante. Des rapports de presse rejettent le blâme sur la pauvreté incitant la surpêche avec des filets interdits, par les pêcheurs artisanaux. En utilisant des méthodes qualitatives et en puisant dans les connaissances des intervenants locaux, cette étude dissipe les points de vue simplistes en explorant en profondeur le secteur de la pêche artisanale dans les zones côtiéres du Pakistan et en documentant les défis soulevés par celui-ci. Nous soutenons que le lien pauvreté – dégradation des ressources est connecté à travers un lien du marché pauvreté-crédit et que résoudre ce probléme permettra d'adresser les imperfections dans les marchés du crédit et des produits. En outre, nous soutenons que la dégradation de la pêche a également d'autres causes et que résoudre ces problèmes et favoriser l'autonomisation des communautés réduira le lien pauvreté-dégradation des ressources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".