Science, decision‐making and development: managing the risks of climate variation in less‐industrialized countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it