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Record W2963571668 · doi:10.1080/02723638.2019.1643172

Aging and the changing urban environment: the relationship between older people and the living environment in post-reform Beijing, China

2019· article· en· W2963571668 on OpenAlexaff
Jie Yu, Mark W. Rosenberg

Bibliographic record

VenueUrban Geography · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBeijingRelocationChinaAging in placeUnit (ring theory)PerceptionOlder peopleInequalityEconomic growthPopulation ageingPopulationSocioeconomicsGeographySociologyGerontologyPsychologyDemographyMedicine

Abstract

fetched live from OpenAlex

The Chinese population is aging rapidly. In Beijing, older people’s immediate environments have seen great changes and are important for their everyday aging experiences. This study explains the changing relationships between older people and their living environments by conducting in-depth interviews with 47 older people living at home in Beijing. Results show that older people’s perceptions of their living environments can be categorized by their relocation experience, residential and neighborhood type. Growing housing inequality in post-reform urban China reflects how older people assess their built environments changes. Social relations have changed tremendously with the formation of new social and residential spaces in the city, especially for those who have relocated to different types of residences. Current urban spaces create a sense of insecurity for the older generation that is familiar with the previous Work-unit system.

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)0.001
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 score0.087
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations61
Published2019
Admission routes1
Has abstractyes

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