Bibliographic record
Abstract
This book focuses on Canadian oceans policy since the signing of the 1982 United Nations Convention of the Law of the Sea at Montego Bay, Jamaica.The articles presented here by political scientists, economists, lawyers, scientists, and diplomats provide a multidisciplinary perspective on the problems and possibilities of transforming a legal document into an effective international oceans regime.The central theme of the book is the adequacy of Canada's response to the new regime.The individual chapters present clear and readable summaries of where Canada stands in giving practical substance to the principles and provisions of the 1982 Convention and in resolving the complex array of legal and political problems which stand in the way of a widely accepted oceans regime.The contributors are united in their conclusion that much remains to be done to ensure that the objectives of the Convention are not dissipated by those states that disagree with some of its aspects.As a major beneficiary of the proposed regime, Canada will have to design a more tightly integrated and co-ordinated oceans policy as well as show a willingness to continue to play an active role as a reconciler of international differences.Canadian Oceans Policy makes a major contribution to our understanding of an increasingly vital area of global politics.It will be of interest to both academics and policymakers and to all those concerned with the future of the oceans.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.723 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".