Marine resources : property rights, economics and environment
Bibliographic record
Abstract
Part 1 Legal and Regulatory Issues: Introduction, M. Falque Property rights and seabirds, A. Charlez Fertilization of the open ocean - effects of private property rights, economics and the environment, M. Markels, Jr. Fisheries, property rights and regulation of fisheries in Ancient Rome - Nihil Noui Sub Mari, Y. Peuriee Existing law - a help or a hindrance? A case study of marine ranching, H. Pickering European and French sea fisheries legislation in search of individual transferable quotas, J.-L. Prat Property rights and fisheries in OECD countries, C.-C. Schmidt Property rights and marine pollution, P. Simon The evolution of the UK Fisheries management - an overview, G. Valatin. Part 2 Economics: Introduction, H. Lamotte Property rights and marine resources - theoretical foundations and applications, J.-P. Centi Equity and management instruments in fisheries, J. Catanzano, S. Cunningham Overcoming the new tragedy of the commons - a commercial framework is inevitable, R. Edwards, M. Smallridge Modeling individual transferable quotas for renewable resources, L.-P. Mahe, C. Ropars Managing renewable resources - theoretical and empirical issues for the case of fishing resources, S. Mairesse, V. David Economic, institutional and social conditions for efficient property rights, H. Rey-Valette Beyond regulatory solutions - controlling overfishing with access controls, J.-P. Troadec Do private property rights lead to the sustainable development and management of marine resource? An analysis, C. Vanderstricht. Part 3 Institutions: Introduction, M. Falque Implementation and future of marine property rights, R. Beattie Saving Canada's fisheries - why we should move from government regulation to systems of self-managed ownership, E. Brubaker Fisheries policy of Russia in 1998-1999, V.V. Chevtchenko Remapping the waters - the significance of sea tenure-based protected areas, J. Cordell Private property rights, markets and regulation in the 21st Century, M. De Alessi When ideas conspire with circumstances - introducing individual transferable quotas in Iceland's fisheries, H.H. Gissurarson Co-management and crisis in fisheries science and management, B.J. McCay Privatization of marine resources in European Union countries - an overview of national situations, C. Nordmann Artificial reef immersions in the Languedoc-Roussillon coastal zone, B. Pary General report and final remarks, H. Lamotte.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".