Groundwater governance: a review of the assessment methodologies
Bibliographic record
Abstract
Groundwater, the world’s largest and most exploited freshwater resource is a crucial ingredient for global socio-economic development. However, the domination of human-induced drivers such as climate change, rapid demographic escalation, alteration in land use, industrialisation, and an increase in water demand has further stressed the unfrozen freshwater resources. This review provides a comprehensive literature-based analysis on different assessment methodologies for groundwater governance, and critically analysed the applicability and knowledge gaps in the assessment methodologies for evaluating groundwater governance under climatic and nonclimatic stresses. Furthermore, in the absence of a designated groundwater governance framework under stress, the study emphasized the need for developing a ready-to-use groundwater governance framework to assess the existing state of governance, tackling the prevailing knowledge gaps. A multidimensional framework consisting of key groundwater governance elements, the inclusion of the vulnerable and marginalised groups, current and future stressors, and an approach for aggregating multiple elements would overcome the limitations in previous assessment methodologies. Additionally, this framework would contribute to understanding current governance provisions and the capacity to manage those provisions, realise the strengths, gaps, and areas for improvement, and quantitatively visualise the prevailing state of groundwater governance for planning multiple strategies to possible threats and conflicts from the stresses.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".