Usable Science? The U.K. Climate Projections 2009 and Decision Support for Adaptation Planning
Bibliographic record
Abstract
Abstract With future changes in climate being inevitable, adaptation planning has become a policy priority. A central element in adaptation planning is scientific expertise and knowledge of what the future climate may hold. The U.K. Climate Projections 2009 (UKCP09) provide climate information designed to help those needing to plan how to adapt to a changing climate. This paper attempts to determine how useful and usable UKCP09 is for adaptation decision making. The study used a mixed-methods approach that includes analysis of adaptation reports, a quantitative survey, and semistructured interviews with key adaptation stakeholders working in the science–policy interface, which included decision makers, knowledge producers, and knowledge translators. The knowledge system criteria were used to assess the credibility, legitimacy, and saliency of UKCP09 for each stakeholder group. It emerged that stakeholders perceived UKCP09 to be credible and legitimate because of its sophistication, funding source, and the scientific reputation of organizations involved in UKCP09’s development. However, because of the inherent complexities of decision making and a potentially greater diversity in users, UKCP09’s saliency was found to be dependent upon the scientific competence and familiarity of the user(s) in dealing with climate information. An example of this was the use of Bayesian probabilistic projections, which improved the credibility and legitimacy of UKCP09’s science but reduced the saliency for decision making. This research raises the question of whether the tailoring of climate projections is needed to enhance their salience for decision making, while recognizing that it is difficult to balance the three knowledge criteria in the production of usable science.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".