Archetypical opportunities for water governance adaptation to climate change
Bibliographic record
Abstract
We explore opportunities for climate adaptation in the context of water governance.We focus on opportunities linked to the provision of climate information, raising the question of whether they are limited to incremental adaptation, or can also bring about transformational adaptation.We address this question through an archetype analysis based on 26 peer-reviewed articles.In each article, opportunities are identified, coded using the social-ecological system framework, and then bundled into archetypes that encompass similar opportunities reappearing across multiple cases.Results suggest that the provision of climate information can constitute an opportunity for adaptation that goes beyond purely incremental adjustments to a changing climate.Specifically, two of the six archetypes identified enable transformational adaptation by bringing long-term implications of current impacts into focus and by addressing the issue of capacity of existing institutions to respond to climate change.However, there is a high degree of heterogeneity in the characterization of opportunities, and the six archetypes only cover about one in three of the opportunities identified.This indicates the need for further research to develop more streamlined conceptualizations.In this respect, the archetypes identified herewith suggest some avenues for further conceptual development.We also explore policy implications, raising questions regarding the current development of climate services.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".