‘Enabling places’: Rethinking ‘community’ in ageing‐in‐community in Beijing, China
Bibliographic record
Abstract
OBJECTIVE(S): To understand how community as 'enabling places' is experienced by older people and brings about enabling resources for supporting ageing in community (AIC). METHODS: From a health geographical perspective, we conceptualize community as enabling places that are produced by the interaction of material, social, and symbolic resources. Focusing on a community-based care centre (CBCC) in Beijing, China, we conducted semi-structured interviews with 17 older persons to examine how a CBCC enabled AIC. RESULTS: The CBCC site created three interdependent spaces and material/social/affective resources for enabling AIC: (1)living space (residential care beds) to create a sense of connection and safety; (2) a CBCC-supported care space at home to create an atmosphere of trust and safety; and (3) a social space to create feelings of belonging and contribution. Variations in how the three resources interacted produced not only different spaces at the same site for various users but also different AIC experiences for the same user. CONCLUSIONS: Community is not simply a static research context or spatial container. Rather, community as an enabling place involves a dynamic process in which spatial/social/affective resources are encountered and interact. Older people's AIC experiences change as their encounters change in the three types of resources we described and thus their capacities for ageing well change correspondingly. Furthermore, the binary idea of community versus institution needs to be expanded to explore how home, community, and institution are related, in order to create enabling spaces for AIC.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".