La consolidation de l’Europe bleue : nouveau contexte international et nouveaux enjeux
Bibliographic record
Abstract
Each of the member states of the European Economic Community (EEC) has extended, through a common agreement, its own fishing grounds to 200 miles, thus leading to the creation, since 1977, of the Community waters whose exploitation would be subjected to the common fisheries policy of the EEC. The widespread extension of fishing grounds throughout Europe together with the state of overfishing in the North-East Atlantic have led the EEC to elaborate a policy in order to protect the interests of its member states, to make their fishing vessels competitive, and to ensure the stability of the fishing industry. This paper looks into the implementation of the fisheries policy of the EEC, internally — namely access s rights to Community waters, the coordination of markets and producers, aid to modernize the vessels - as well as regarding foreign countries with whom agreements are sought in order to maintain historic fishing rights - specially in the North Atlantic - or in order to develop new fishing grounds - specially along the West African coast and in the Indian Ocean - a quarter of the EEC catch is made outside Community waters. France is deeply committed to the orientations of the EEC fisheries policy due to the importance of its fleet of trawlers fishing outside French waters and to the potential catch in the exclusive economic zone of its departments and territories overseas. The compromise signed by member states in 1983 is an important step towards the establishment of a true « Europe Fisheries ».
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".