Uncertainty and precariousness at the policy–science interface
Bibliographic record
Abstract
This chapter examines the affective dimension of climate change adaptation decision-making. Using three case studies, the authors describe different experiences of precariousness and argue that this emotion helps explain scientist and decision-makers (in)actions at the science–policy interface. The first case looks at water management decision-making in Brazil and involves technocrats, local stakeholders, and public officials in water governance in the state of Ceará, in Northeast Brazil. It examines the dilemmas of participatory and integrated water management in the context of scarcity and the ability to predict future climate. The second case shows how local decision-makers, such as planners, are developing and implementing adaptation plans in the absence of leadership and support from state and federal levels. In the third case, the focus is on how the adaptation science–policy interface generates new forms of vulnerability in coastal communities in New Brunswick, Canada. The authors illustrate the complexities of decision-making at the science–policy interface under conditions where different actors are operating under different types of uncertainty that can leave them more of less vulnerable. Using the concept of precariousness may serve to gain a better understanding of how uncertainty is lived and managed in both science and decision-making.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".