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Record W1967972637 · doi:10.1111/roiw.12114

Measuring Economic Insecurity in Rich and Poor Nations

2014· article· en· W1967972637 on OpenAlex
Lars Osberg, Andrew Sharpe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Income and Wealth · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsCanadian Standards AssociationDalhousie University
Fundersnot available
KeywordsPovertyEconomicsLivelihoodIndex (typography)Food securityDevelopment economicsDeveloping countrySocial securitySocioeconomic statusEconomic growthPublic economicsAgriculturePopulationGeographyEnvironmental health

Abstract

fetched live from OpenAlex

Worrying about possible future economic dangers subtracts from the present well‐being of individuals, which is why affluent societies have complex systems of private insurance and public social protection to provide a degree of economic security. However, such protections are largely unavailable to the citizens of poor nations (i.e., most of humanity). How can one measure economic security in these very different contexts? This paper examines trends in the IEWB Economic Security Index for four affluent OECD countries and compares a cross‐section of 70 rich and poor countries in 2007/08. To reflect better the reality of developing countries, it revises the IEWB index to: (1) include the volatility of food production in the risk of loss of livelihood; (2) adjust the risks of health care costs to consider the proportion of household spending on food (which is non‐discretionary, and large in poor countries); and (3) add adult male mortality to the risk of divorce in calculation of the risk of single parent poverty.

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.

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)0.000
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 score0.351
Threshold uncertainty score0.075

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.235
Teacher spread0.221 · 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