Integrated Coastal Management (ICM): A Brief Legal And Institutional Comparison Among Canada, The United States And Mexico
Bibliographic record
Abstract
A recent study by the Food and Agricultural Organization of the United Nations (FAO) determined that 20.6% of the world's population currently lives within 30 km of the nearest coastline, 29.2% within 60 km, 35% within 90 km, and 39.5% within 120 km. By the year 2050 more people will live within 120 km of the coastline than are alive in the world today. Canada, the United States, and Mexico are adjacent coastal nations where the impact of significantly increased human activity in the coastal zone by the year 2050 may be potentially catastrophic. Integrated coastal management (ICM) may have a role to play within, and between, all three countries to help ameliorate this situation. The objectives of this paper are threefold. First, it seeks to define what is meant by the term "ICM." Second, it seeks to describe the current legal context for ICM in Canada, the United States, and Mexico. Third, it seeks to identify "lessons" that Canada, the United States, and Mexico can learn from each other with a view towards the more sustainable management of the coastal zones within and between all three countries. This paper concludes that there are a number of key gaps in the way that ICZM issues in all three countries are currently being addressed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".