Science, decision‐making and development: managing the risks of climate variation in less‐industrialized countries
Bibliographic record
Abstract
Abstract This article addresses the role of scientific knowledge in decision‐making with respect to climate variability and change in the developing world, with a focus on scientific capacity. We propose a ‘systemic’ view of scientific capacity for studying the relationship between science and decision‐making vis‐à‐vis climate variation, one that encompasses knowledge production, as well as its translation for and use in decision‐making. We analyze the challenges faced by developing countries in building capacity on each of these elements. Case studies on the production and use of scientific information for societal decision‐making at three distinct timescales—the weekly scale (Hurricanes in the North Indian Ocean), the seasonal scale (Climate Variability in the Sahel), and the decadal/century scale (Climate Change Impacts on Small Island States) are used to elucidate the scale and complexity of capacity building challenges. We argue that capacity building for coping with the impacts of climate change is interwoven with the capacity needed for meeting the challenges of development, particularly those related to short‐term climate and weather variation. Any serious attempt to build scientific capacity for decision‐making vis‐à‐vis climate change will need to embrace a ‘developmentalist’ position. WIREs Clim Change 2011 2 201–219 DOI: 10.1002/wcc.98 This article is categorized under: Climate and Development > Knowledge and Action in Development Social Status of Climate Change Knowledge > Climate 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".