LONG-TERM POVERTY, SPATIAL DISADVANTAGE, AND MULTIPLE EXCLUSIONS IN LATER LIFE: A CASE IN SHANGHAI
Bibliographic record
Abstract
Abstract Objectives: This study aimed to examine the associations among three types of cumulative disadvantages: long term poverty, spatial disadvantage, and multiple exclusions using a Cumulative dis/advantage (CDA) and life course perspective. Method: A sample of 419 Chinese adults aged 60 and older from three communities in Shanghai completed a structured questionnaire. Multiple exclusions were measured by variables related to material resources, housing conditions, social relations, civic activities, basic services, and neighbourhood factors. Hierarchical regression was implemented by SPSS 25 and moderation analysis was performed with the SPSS macro PROCESS from Hayes (2013). Results: 39% of respondents reported that they experienced multiple exclusions and one in five respondents report often or most time living in poverty. Regression analysis indicated that experience long-term poverty and length of living in the same neighbourhood is positively associated with multiple exclusions in later life and these associations are not attenuated by demographics, and health factors. But, moderation analysis showed the length of living in the same neighbourhood has significant moderating effect on the relationship between long term poverty and multiple exclusions, particularly for older adults living in the same neighbourhood for more than 30 years. Discussion: The study findings illustrate the need to consider not only life course risks such as long-term poverty but also spatial disadvantages in addressing multiple social exclusions among older Chinese adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".