The potential for adaptive water governance on the US–Mexico border: application of the OECD's water governance indicators to the Rio Grande/Bravo basin
Bibliographic record
Abstract
Abstract Despite decades of political commitments, laws and agreements and significant policy effort, the governance system in the Rio Grande/Bravo basin is not able to meet the water demands generated by a growing region. Long stretches of the river are completely dry for much of the year, and water managers cannot meet full allocations to water users, let alone ensure water quality and quantity for environmental services and sustainability. Both academic scholarship and policy analysis attribute failures such as this to the inability of current water governance regimes to respond to rapidly changing circumstances – to ‘adapt’. The adaptive governance literature calls for resource management regimes that are distributed yet coordinated through polycentric arrangements, as well as flexible; that promote broader engagement and that generate and disseminate knowledge as well as stimulate learning in the face of complexity and uncertainty. This paper reports on the results of qualitative empirical research which applies the OECD's water governance indicators as a diagnostic tool in order to identify the most significant adaptive governance gaps in the transboundary Rio Grande/Bravo basin.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".