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Record W2067444697 · doi:10.5751/es-05252-180116

Effects of Educational Attainment on Climate Risk Vulnerability

2013· article· en· W2067444697 on OpenAlexvenueno aff
Erich Striessnig, Wolfgang Lutz, Anthony Patt

Bibliographic record

VenueEcology and Society · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
FundersAustrian Science Fund
KeywordsVulnerability (computing)Climate changePsychological resilienceNatural disasterEmpirical evidenceCoping (psychology)Adaptive capacityContext (archaeology)Educational attainmentClimate riskNatural resource economicsDevelopment economicsEnvironmental resource managementGeographyEnvironmental planningEconomic growthEconomicsPsychologyEcologySocial psychology

Abstract

fetched live from OpenAlex

In the context of still uncertain specific effects of climate change in specific locations, this paper examines whether education significantly increases coping capacity with regard to particular climatic changes, and whether it improves the resilience of people to climate risks in general. Our hypothesis is that investment in universal primary and secondary education around the world is the most effective strategy for preparing to cope with the still uncertain dangers associated with future climate. The empirical evidence presented for a cross-country time series of factors associated with past natural disaster fatalities since 1980 in 125 countries confirms this overriding importance of education in reducing impacts. We also present new projections of populations by age, sex, and level of educational attainment to 2050, thus providing an appropriate tool for anticipating societies' future adaptive capacities based on alternative education scenarios associated with different policies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations130
Published2013
Admission routes1
Has abstractyes

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