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Record W2795337566 · doi:10.1111/ajag.12511

Role of income in intergenerational co‐residence: Evidence from selected African and Asian countries

2018· article· en· W2795337566 on OpenAlexaff
Nusrate Aziz, Belayet Hossain, M. Shahe Emran

Bibliographic record

VenueAustralasian Journal on Ageing · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsOkanagan CollegeThompson Rivers UniversityAlgoma University
Fundersnot available
KeywordsResidenceLife expectancyDemographic economicsDeveloping countryEconomicsEconomic growthDemographySociologyPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: The study investigates the macroeconomic determinants of co-residing arrangement between generations in selected developing countries with a focus on examining the effect of changing income level of the working generation. METHODS: A reduced form model is specified for co-residence between the older generation and altruistic working generation. The fixed- and random-effects models are applied in two waves of data for 22 countries. RESULTS: Estimated results indicate that the income of the altruistic working generation has a negative effect on co-residence, suggesting that if the income of the working generation increases, co-residence decreases. This decrease is greater for older men compared with their female counterparts. Life expectancy, literacy and culture also have significant influences on co-residence. CONCLUSION: Co-residence is expected to fall in developing countries with economic growth over time. Consequently, a higher proportion of older citizens will be vulnerable in the future. Hence, governments of developing countries will face increasing pressure from their older people to provide appropriate planning and strategy to face this challenge.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.296
Teacher spread0.282 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

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