A framework for identifying and selecting long term adaptation policy directions for deltas
Bibliographic record
Abstract
Deltas are precarious environments experiencing significant biophysical, and socio-economic changes with the ebb and flow of seasons (including with floods and drought), with infrastructural developments (such as dikes and polders), with the movement of people, and as a result of climate and environmental variability and change. Decisions are being taken about the future of deltas and about the provision of adaptation investment to enable people and the environment to respond to the changing climate and related changes. The paper presents a framework to identify options for, and trade-offs between, long term adaptation strategies in deltas. Using a three step process, we: (1) identify current policy-led adaptations actions in deltas by conducting literature searches on current observable adaptations, potential transformational adaptations and government policy; (2) develop narratives of future adaptation policy directions that take into account investment cost of adaptation and the extent to which significant policy change/political effort is required; and (3) explore trade-offs that occur within each policy direction using a subjective weighting process developed during a collaborative expert workshop. We conclude that the process of developing policy directions for adaptation can assist policy makers in scoping the spectrum of options that exist, while enabling them to consider their own willingness to make significant policy changes within the delta and to initiate transformative change.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".