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Household Vulnerability and Resilience to Shocks in Egypt

2022· book-chapter· en· W3006134606 on OpenAlexaboutno aff
Imane Helmy, Rania Roushdy

Bibliographic record

VenueOxford University Press eBooks · 2022
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsShock (circulatory)EconomicsPsychological resilienceVulnerability (computing)Demographic economicsCoping (psychology)Quarter (Canadian coin)Consumption (sociology)AgricultureSocioeconomicsDevelopment economicsGeographyPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

Exposure to shocks reduces households’ economic resilience. While all households are negatively affected by various shocks, poor households are more likely to be exposed to different risks. This chapter uses the 2018 round of the Egypt Labor Market Panel Survey (ELMPS) to examine the nature of shocks experienced by households in different socio-economic groups and the ex-post coping mechanisms that they adopted. The results show that almost a quarter of the Egyptian households experienced food insecurity either solely or in combination with shocks. Economic shocks were the most common distress followed by health shocks during the year preceding the ELMPS interview. Households used consumption rationing or depended on their social capital as a coping mechanism. Households whose heads had less education, worked in the informal private sector or agriculture, or were self-employed were more likely to have experienced a shock. Households residing in rural areas, particularly in Upper Egypt, or with large families were more vulnerable to shocks.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.193
Teacher spread0.171 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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