Green paradiplomacy and water resource management in North America: the case of the Great Lakes-St. Lawrence River Basin
Bibliographic record
Abstract
Sharing the world's largest freshwater lake system, Canada and United States have for over a hundred years sought to jointly manage this vital resource. However, in accordance with multi-level governance and paradiplomacy literature, it appears that this collaboration has considerably changed over the last thirty years. From an initial bilateral cooperation between federal authorities, provinces and US states became prominent actors in cross-border water governance, and, in this sense, a green transboundary paradiplomacy has emerged along the 49th parallel. In particular, a specific cross-border organization, the Council of Great Lakes Governors, developed an interesting water regime, and adopted recently a dual tool for water governance in 2005, called the “Great Lakes – St. Lawrence River Basin Water Resources Compact” and its non-binding twin the “Great Lakes – St. Lawrence River Basin Sustainable Resources Agreement”, which aim to prevent massive water transfer outside the basin. Adopting a green paradiplomacy and multi-level governance perspective, this article aims to analyze in depth this new environmental regime and the legislative implementation process of this dual agreement. Then, we will begin a broader reflection on cross-border and subnational environmental governance in North America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".