A global dataset on subsidies to the fisheries sector
Bibliographic record
Abstract
This article contains data on subsidies provided to the fisheries sector by maritime countries. The dataset is the culmination of extensive data collection efforts using peer-reviewed and grey literature, national budgets, online databases, websites and other relevant sources (e.g. OECD, World Bank and WTO), in order to estimate the scope and magnitude of global fisheries subsidies. For subsidies where we found evidence of expenditure by a country, we record the total amount alongside the source references and refer to these as ‘reported’ data. Where evidence is found that a country provides a subsidy but no amount reported, we estimate using various approaches and refer to these as ‘modeled’ data. Where evidence exists that no subsidy is provided by a country we refer to these null values as ‘not found evidence of subsidy’. All amounts were converted to constant 2018 USD using 2017 exchange rates and annual Consumer Price Index averages. The final dataset of ‘reported’, ‘modeled’ and ‘not found’ subsidies for 2018 consists of 13 subsidy types across 152 maritime countries. The dataset, first developed in the early 2000s, now forms part of the global fisheries management infrastructure and is a central tool used by WTO negotiators. The data we provide may be used to support local, regional and global fisheries management decision-making and may have further uses when analysed in combination with other fisheries related data. Interpretation of these data can be found in the associated research article titled “Updated estimates and analysis of global fisheries subsidies” [1].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.022 |
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".