Bibliographic record
Abstract
Abstract Subsidies to the fishing industry are common worldwide, and it is well accepted that these subsidies contribute to overcapacity in fishing fleets and overexploitation of fisheries resources. To date, however, most of the quantitative estimates of these subsidies reported in the literature have been at either the multicountry or global level. Estimates are rarely based on a detailed accounting of individual subsidy programs, limiting both their accuracy and usefulness for management decisions. The present analysis helps fill this gap with respect to U.S. fisheries subsidies. Here, we report estimates of the different types of subsidies paid to the fishing sector by different levels of government in the USA. Our analysis shows that from 1996 to 2004, the U.S. fishing industry received a total of US$6.4 billion (1 billion = 109) in government subsidies (an average of $713 million per year), federal funds accounting for 79% of this total. This estimate is conservative because it does not include funding for fisheries management, port construction and maintenance, or subsidy program administration. Federal and state fuel subsidies (44% combined) and federal and state fisheries research (40% combined) accounted for the vast majority of fisheries subsidies. The next three largest subsidies were state sales tax subsidies (5%), disaster aid (4%), and fishing access payments (3%). Distribution was heavily weighted toward Alaska and the western Pacific and toward Pacific salmon Oncorhynchus spp. and tunas (family Scombridae). Similar detailed examinations of fisheries subsidies in other countries will be necessary in the likely event that the World Trade Organization establishes rules prohibiting subsidies that contribute to overcapacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".