Bibliographic record
Abstract
Watershed boundaries are widely accepted by many water practitioners and researchers as the de facto ideal boundary for both water management and governance activities. In governance, watershed boundaries are typically considered an effective way to integrate the social, political, and environmental systems they encompass. However, the utility and authenticity of the watershed boundary for water governance should not be assumed. Instead, both scholars and practitioners ought to carefully consider the circumstances under which watershed boundaries provide an appropriate frame for governance. The purpose of this paper is to identify how water governance can transcend the watershed boundary. An empirical case study of governance for water in Ontario, Canada, reveals boundary-related challenges. In this case, issues relating to boundary selection, accountability, participation and empowerment, policysheds and problemsheds reveal the strengths and weaknesses of relying on watershed boundaries as a frame of reference for governance. The case also highlights promising alternatives that are being used to transcend the watershed boundary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".