Early Warnings, No Actions: A Practice Perspective on Barriers to Anticipatory Action Approaches
Bibliographic record
Abstract
ABSTRACT Within the manifold approaches of climate adaptation efforts and resilience building, anticipatory action (AA) presents a promising, novel approach that emphasizes acting before a disaster strikes, shifting from reactive crisis response to proactive preparedness. Taking a management and coordination perspective, this paper analyzes challenges to the successful implementation of AA. Drawing on interviews, focus group discussions, meetings and observations with local communities, AA practitioners, local governments and the implementing humanitarian agency in flood‐prone regions of Nigeria, this paper identifies five key barriers to AA. These barriers include conflicting timeframes between actors, tensions between short‐term feasibility and long‐term needs, competing priorities between anticipatory and reactive approaches, structural challenges in integrating AA into existing systems, and trade‐offs related to the reliability and credibility of forecasting data. The findings show that these barriers are not isolated or stable, but co‐enacted through interrelated practices of multiple actors involved in implementing AA. Adopting a practice perspective on barriers reveals how misalignments in temporalities, priorities, structures, and scales are co‐constructed, helping to explain their persistence. We argue that addressing these challenges requires a shift from technical fixes of AA toward a systemic perspective that understands AA as a dynamic and complex governance challenge.
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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 | 0.089 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".