Bibliographic record
Abstract
Examining fisheries, Brexit, the Trade and Cooperation Agreement (TCA) and its consequences for the Fishing Industry in the UK and the EU, this book explores key issues within the complex topic of fisheries after Brexit. Assessing the new fishing relationship between the UK and the EU, which will continue to develop over the next decade, it provides an important study of the state of fisheries post-Brexit. Taking a cross-cutting economic, legal and policy approach, the book outlines the social and economic impacts of Brexit on the UK and EU fishing industries. It critically analyses the provisions relevant to fisheries in the TCA, reflects on the bilateral fishing negotiations between the EU, UK and Norway, providing inferences as to what the "new and special relationship" might be in fisheries. It then focuses on the 2020 Fisheries Act and explores internal divergences in the nations of the UK because of devolution. Taking an international approach, the work offers an exploration of cooperation in fisheries enforcement, international and regional obligations in marine conservation, and the new horizons for the UK in international fisheries organizations and arrangements now it is no longer a member of the EU. It offers an overview of expert opinion on fisheries post-Brexit, highlighting lessons learned and future developments for fisheries in a post-Brexit world. Having finally signed the Trade and Cooperation Agreement on 31 December 2020 after tense negotiations, the United Kingdom and European Union have found themselves in a new fisheries relationship. This book maps the complex social, economic, legal and policy issues of fisheries in a post-Brexit world and will be of interest to stakeholders and scholars.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".