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Record W2911439481 · doi:10.3390/ijerph16030488

Mobilities of Older Chinese Rural-Urban Migrants: A Case Study in Beijing

2019· article· en· W2911439481 on OpenAlexaff
Yang Cheng, Mark W. Rosenberg, Rachel Winterton, Irene Blackberry, Siyao Gao

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of ChinaLa Trobe University
KeywordsBeijingMobilitiesEnvironmental healthChinaGeographyGerontologyEconomic geographySocioeconomicsDemographic economicsDemographyMedicineSociologyEconomics

Abstract

fetched live from OpenAlex

Along with the rapid urbanization process in Beijing, China, the number of older rural-urban migrants is increasing. This study aims to understand how Chinese rural-urban migration in older age is influenced by, and impacts on the migrants' mobilities. This study draws on a new conceptual framework of mobile vulnerability, influenced by physical, economic, institutional, social and cultural mobility, to understand older people' experiences of migration from rural to urban areas. Forty-five structured in-depth interviews with older rural-urban migrants aged 55 and over were undertaken in four study sites in Beijing, using the constant comparative method. Results demonstrate that rural household registration (hukou) is an important factor that restricts rural older migrants' institutional mobility. As older migrants' physical mobility declines, their mobile vulnerability increases. Economic mobility is the key factor that influences their intention to stay in Beijing. Older migrants also described coping strategies to improve their socio-cultural mobility post-migration. These findings will inform service planning for older rural-urban migrants aimed at maintaining their health and wellbeing.

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)0.004
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 score0.021
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.041
GPT teacher head0.406
Teacher spread0.365 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

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