Reflecting on twenty years of international agreements concerning water governance: insights and key learning
Bibliographic record
Abstract
The purpose of this article is to examine the research advanced in the journal, International Environmental Agreements: Politics, Law and Economics that represents key insights into international agreements on water and their political, legal, economic and cross-disciplinary dimensions for water governance. The article analyses evidence and lessons learnt over the last twenty years to inform policy through a review of theoretical advances, innovations in principles and policy instruments, outcomes of problem-solving and knowledge gained regarding water agreements and associated institutions. Important international agreement principles of no significant harm and economic frames of water as a 'commons' advance equity and community of interest in relation to water. The studies on water, sanitation and hygiene point to the ways the role of the state can be advanced in achieving Sustainable Development Goals and in complex contexts of water scarcity and public private partnerships. Cross-disciplinary learnings substantiate the existence and utility of multiple water frames in legal arrangements and use of multiple policy instruments. Cross-disciplinary insights are significant in addressing equity, whether through the nascent development of water indicators or in advancing social learning. Water governance frameworks increasingly focus on adaptation by incorporating multiple stakeholders. These findings that advance equity and inclusivity are tempered by crucial lessons in our understanding of the very contested, power-laden nature of water governance that impact agency at multiple scales and policy coordination across sectors of water, food and energy.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| 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".