Relational Geographies of Community Care for Older People in Urban Beijing, China
Bibliographic record
Abstract
With the paradigm shift of the place of care for older people from institution to community since the 1990s, the concept of community has re-invigorated aging research. Based on a relational approach, this thesis develops a heuristic framework of two co-constitutive triads. One triad is to unpack how community care manifest in three interdependent processes of policymaking (macro), localization (meso), and experience (micro). The other triad is to unpack community as a three-dimensional concept (material, social, and symbolic) to bridge trans-scaled spaces and places together. Specifically, this thesis seeks to answer four interrelated research questions: 1) What is the trans-scaled relationality of community in care for older people in the health geography literature? 2) What is the role of community in shaping “macro” policies of care for supporting community-dwelling older people? 3) What is the role of community in “meso” embedding care resources and spaces into local communities? and 4) What is the role of community in shaping “micro” experiences of care at a community-embedded facility? The findings demonstrate not only the material, social, and symbolic meanings of community at each process but also the trans-scaled relationality of these meanings: 1) Materially, lived experience of a community care facility is shaped not only by that facility (micro) but also by the processes of embedding the facility into the community (meso) and defining spatial units for allocating facility resources (macro); 2) Socially, social interactions of older people within that facility (micro) are shaped by the negotiation between different stakeholders in site-locating and relation-building (meso) that also relates to the negotiation of the state, market, and the voluntary sector (macro) ; and 3) Symbolically, ideas about what community should be like may converge or diverge between older people's affective senses (micro), stakeholders' concerns on the operation (meso), and normative principles to policymakers and wider society (macro). The thesis joins the recent “relational turn” in health geography by conceptualizing trans-scaled relationality of spaces and places through community. The findings can also inform strategically placing community care in China, Canada, and other countries where new models of community care for older people are being developed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".