(Mis)communicating climate change? Why online adaptation databases may fail to catalyze adaptation action
Bibliographic record
Abstract
Over the last decade a plethora of action‐oriented research projects has been conducted in developing countries, exploring how to effectively adapt to the anticipated impacts of climate change. Many intergovernmental agencies and development organizations have chosen to disseminate their research results via online databases. It is unclear, however, whether these databases are useful in terms of actual adaptation planning and implementation. A systematic review of online databases has found at least 64 databases and tools online related to climate change adaptation. Despite the abundance of databases, this analysis reveals that the existing body of online databases generally lack the structure and mechanics to identify, extract, and synthesize both effective and ineffective climate change adaptation practices, projects, programs, and policies. Even relatively basic information, such as identification of projects’ projected versus actual costs is absent, which are crucial decision‐making criteria particularly in developing country contexts where resource constraints are significant. In this paper we evaluate these online tools with a focus on identifying features that potentially could contribute to knowledge sharing and successful exchange of climate change adaptation projects and practices within a developing country context. We conclude the paper with recommendations for how to improve efforts to communicate climate change research, such as more nuanced needs assessments of potential users of databases. WIREs Clim Change 2016, 7:600–613. doi: 10.1002/wcc.401 This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation Climate and Development > Knowledge and Action in Development Social Status of Climate Change Knowledge > Knowledge and Practice
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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 | 0.081 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".