Eldercare and re-purposing the built environment of urban China: Capital switching and bundled commodities in recent trends
Bibliographic record
Abstract
This article considers the national priority recently accorded to institutional eldercare in the People's Republic of China, with particular emphasis on the re-purposing of existing buildings to found new facilities. This new approach is noteworthy in the very parts of the country that for decades now have been profoundly committed to new construction, and in a country that has retained for so long deep commitments to at-home, family-centred eldercare as a feature of filial piety. Rapid demographic aging and heavy migration of younger adults for employment and education are among the forces that have eroded the practical foundations for this older, valued mode of eldercare. But the present article moves beyond other important social policy questions associated with institutionalized eldercare, to consider the re-purposing of buildings for this purpose. We argue that this might also point to a form of 'capital switching' at work in the face of over-accumulation in fixed capital and other elements of the built environment. After an initial period of building new facilities at the suburban interface with the countryside, the turn to abandoned factories, hotels, and kindergartens brings seniors closer to the inner-city neighbourhoods where the clients lived most of their lives. But 'bundling' these commodities with eldercare services to be delivered and paid for short, repeated turnover periods also taps into the value once invested in these abandoned or underutilized facilities. The pervasiveness of profit motivations alongside social policy concerns across nominal state, private-sector, and not-for-profit divides further suggests to us that this pattern in the circulation of capital deserves more attention in eldercare in particular, and current conditions in the real estate markets of urban China suggest that the pattern may be present for researchers to consider more generally.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".