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Record W2900461945 · doi:10.1002/app5.267

Understanding the spatial disparities and vulnerability of population aging in China

2018· article· en· W2900461945 on OpenAlex

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

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
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 head0.084
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