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Record W1997346868 · doi:10.1068/a42503

Heat Waves and Cold Spells: An Analysis of Policy Response and Perceptions of Vulnerable Populations in the UK

2010· article· en· W1997346868 on OpenAlexaff
Johanna Wolf, W. Neil Adger, Irene Lorenzoni

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsMemorial University of Newfoundland
FundersDepartment for Environment, Food and Rural Affairs, UK Government
KeywordsVulnerability (computing)Fuel povertyPerceptionPovertyWarning systemRisk perceptionHeat wavePublic policyCold waveBusinessPolitical scienceGeographyPsychologyEconomic growthClimate changeMedicineEconomicsEngineering

Abstract

fetched live from OpenAlex

Heat waves and cold spells pose ongoing seasonal risks to the health and well-being of vulnerable individuals. Current attempts to address these risks in the UK are implemented through fuel-poverty strategies and heat-wave planning. This paper examines evidence from the UK on whether heat waves and cold spells are addressed differently by public policy in the UK given that risks are mediated by similar perceptions that shape behavioural responses by vulnerable individuals. It is based on a review of UK policies and on a qualitative interview study of risk perceptions of elderly people as a primary identified vulnerable group to these weather extremes. The study involved in-depth repeat interviews with fifteen elderly respondents in summer 2007 and winter 2008 in Norwich (UK). Results suggest that neither heat risks nor cold risks are perceived as personal risks and therefore planned preventive measures by individuals are largely elusive. Cold risk policy reduces vulnerability; policy related to heat relies on early warning and public information programmes and does not reduce underlying vulnerability. Both types of policies largely ignore public perceptions of risks and could benefit from a more cohesive approach, supporting similar measures to reduce seasonal vulnerability.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.311
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations83
Published2010
Admission routes1
Has abstractyes

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