Bibliographic record
Abstract
Adaptation strategies and policies are normally based on climate impact assessments that fail to take account of the social nature and distribution of vulnerability to climate change.This is largely a product of the dominant assessment techniques that are used to inform such strategies and the limits of existing evidence.In this paper I contribute to filling gaps in the current adaptation literature by exploring the social nature of vulnerability and the potential for socially just adaptation.It does so by reviewing studies from the UK, in particular those under the Joseph Rowntree Foundation's Climate Change and Social Justice programme.It finds that vulnerability to high temperatures and fluvial and coastal flooding, in terms of sensitivity, exposure, and the capacity to anticipate, respond, and recover, is concentrated in certain disadvantaged and socially marginalized groups, including those on low incomes.It also finds that both autonomous and planned adaptation may fail to protect the most vulnerable individuals and groups, and may even reinforce existing patterns of vulnerability in some cases, i.e., mal-adaptation, especially where they rely on unmediated market forces or where they fail to explicitly recognize aspects of social vulnerability in their design and implementation.I argue that social justice should be an explicit objective of adaptation strategy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".