Global fisheries economic analysis
Bibliographic record
Abstract
INTRODUCTION The starting point for global fisheries economics work in the Sea Around Us project at the University of British Columbia Fisheries Centre is the creation of global databases. Over the last few years, we have created and/or compiled global databases on ex-vessel fish prices, subsidies, recreational fisheries, social discount rates, and consumer price indices. We are currently developing two additional global databases: cost of fishing and fisheries employment. This information, combined with other project databases, provides remarkable opportunities for conducting global-scale fisheries analyses. This chapter summarizes the results reported by Sumaila et al . (2010), which provide estimates of global fisheries subsidies; and Cisneros-Montemayor and Sumaila (2010) and Dyck and Sumaila (2010), which estimate the contribution of ecosystem-based marine recreation and ocean fish populations to the global economy, respectively. FISHERIES SUBSIDIES WORLDWIDE Fisheries subsidies are defined as financial transfers, direct or indirect, from public entities to the fishing sector, which help the sector make more profit than it would otherwise (Sumaila et al ., 2008). Such transfers are often designed to either reduce the costs of production or increase revenues. In addition, they may also include indirect payments that benefit fishers, such as management and decommissioning programs. Subsidies have gained worldwide attention because of their complex relationship with trade, ecological sustainability, and socioeconomic development. It is widely acknowledged that global fisheries are overcapitalized, resulting in the depletion of fishery resources (Hatcher and Robinson, 1999; Munro and Sumaila, 2002).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".