Watershed or bank-to-bank? Scales of governance and the geographic definition of Great Lakes Areas of Concern
Bibliographic record
Abstract
Much recent scholarship has addressed the rise of the watershed as the preferred scale for the governance of water quality. Although the watershed remains widely perceived as an ideal, “natural” scale of freshwater governance, arguments for the merits of alternative scales and multi-scalar approaches are gaining prominence. The Great Lakes Areas of Concern program, managed jointly by the United States and Canada, represents an important case in which the watershed has not prevailed as the default local scale of governance, at least in the 31 Areas of Concern located in the United States or straddling the international border. Based on a review of documents and analysis of a survey and interviews with key actors from local Areas of Concern, we find considerable variation among U.S. states in the designation of Areas of Concern as watersheds and partial watersheds, bank-to-bank watercourse segments, or hybrids of both. This variation depends not only on the differing biophysical conditions at Areas of Concern but also on differences in the latitude that state agencies gave to local stakeholder groups when the geographical extent of each Areas of Concern was designated and negotiated. In several cases, questions about the appropriate scale of the Areas of Concern led to controversy, with implications for subsequent remediation. We contend that understanding the uneven embrace of the watershed as a scale of water governance requires attending not only to specific governance objectives but also to variations in the relationships between local and subnational scales in governance programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".