Baseline study on the training and certification requirements for fishers in EU Member States
Bibliographic record
Abstract
Training and skills enable fishers to be more efficient, resilient, sustainable and work safer. This is important for one of the world’s most dangerous professions. It is also important in making the profession more attractive in the EU at a time when generational renewal is a major concern. It is also a key plank of the social dimension of the Common Fisheries Policy and the Fisheries and Oceans Pact. A number of existing international treaties and EU Directives already provide frameworks for fisher training and skills, but the degree of compliance with these is not well documented. Moreover, despite the European Commission’s urging, EU Member States have been slow to ratify the key international treaty in this area, namely the International Convention on Standards of Training, Certification and Watchkeeping for Fishing Vessel Personnel (STCW-F) adopted in 1995. Only ten Member States have done so, whereas 22 coastal Member States have fishing fleets. The absence of a harmonised EU-wide approach to the basic safety competences, safe navigation, and safe propulsion of fishing vessels that the STCW-F describes complicates the mutual recognition of certificates, posing barriers to labour mobility and increasing administrative burdens. This lack of alignment also impacts safety, as training standards and competencies vary widely.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".