Analyzing Water Institutions in the 21st Century: Guidelines for Water Researchers and Professionals
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Institutional barriers (as opposed to technical challenges) were identified in the early 1980s as being at the root of many of the world's most challenging water resource problems. To what extent does this claim hold true today? We investigate and report on some major developments which have occurred in the field of institutional analysis over the past 25 years. An article published by Helen Ingram and colleagues in 1984 offering practical advice for water planners undertaking institutional analyses, provides the lens through which we examine changes in the field. Major developments since that article was published include an increasing sensitivity to the importance of context, a heightened awareness of the complex nature of social-ecological systems, and a growing appreciation of the dynamic nature of institutions. Much of the advice offered by Ingram and her colleagues continues to be sound, but we suggest that new insights from the past 25 years of work in this field can enhance the challenge of institutional analyses.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it