Uncertainty and Socioeconomic Vulnerability to Climate Change
Bibliographic record
Abstract
Abstract Addressing uncertainty in vulnerability research in general and quantitative assessments, in particular, has broad societal implications for informing priorities in adaptation and increasing resilience. Qualitative and quantitative vulnerability analyses are frequently used to understand better past, present, and future impacts of environmental stressors on groups of people, infrastructure, and ecosystems; to inform decision- and policymakers about how to mitigate these impacts; and to guide interventions seeking to build community resilience. Increasing climate change impacts raises the importance of understanding, potentially reducing, and managing uncertainty associated with climate change vulnerability assessment. This chapter examines concepts of uncertainty about socioeconomic vulnerability to climatic hazards, discusses different types and potential sources of uncertainty in vulnerability assessments, and illustrates the challenges with two brief case studies. Progress is being made in characterizing these different types of uncertainty; understanding uncertainties involved in creating indices; exploring cascades of uncertainty in multilevel processes linking environment, society, and technology; and identifying societal processes subject to emerging trends, random events, and enduring ambiguity. Continued effort is needed to improve the characterizations of the many forms of uncertainty in socioeconomic vulnerability and then further develop strategies to address or navigate more difficult tasks of reducing uncertainties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".