MétaCan
Menu
Back to cohort
Record W2900461945 · doi:10.1002/app5.267

Understanding the spatial disparities and vulnerability of population aging in China

2018· article· en· W2900461945 on OpenAlexaff
Yang Cheng, Siyao Gao, Shuai Li, Yuchao Zhang, Mark W. Rosenberg

Bibliographic record

VenueAsia & the Pacific Policy Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsQueen's University
FundersFundamental Research Funds for the Central UniversitiesBeijing Normal UniversityNational Natural Science Foundation of China
KeywordsCensusChinaGeographyPopulationPopulation ageingVulnerability (computing)SocioeconomicsEast AsiaRural areaDemographyEconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract Understanding the regional pattern of population aging in China enables rational policy making to address the challenges of inequity in social welfare and care resources among the east–central–west regions and rural–urban areas of China. This study uses census data in 2000 and 2010, and aging population ratios, annual increase rates, and spatial autocorrelation analysis to examine spatial disparities in population aging in China. The results show that the population is more aged and aging more rapidly in rural areas than in urban areas. Spatial clusters of population aging expanded from the east coastal region in 2000, to inland provinces such as Sichuan and Chongqing in 2010. The vulnerable regions in terms of population aging, health status of the elderly population, and economic level at the prefectural level were also identified.

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)0.002
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 score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.363
Teacher spread0.280 · 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

Citations75
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAsia & the Pacific Policy StudiesSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207