Re-purposing the built environment of urban China: Residential eldercare and David Harvey's capital switching in a new era
Bibliographic record
Abstract
In urban China, institutional eldercare interacts with local land markets in ways that present special analytical problems in critical political economy. It is a new sector: home-based eldercare and childcare have formed complementary parts of intergenerational household strategies that are coming under system-wide pressure for the first time. Growth in Chinese institutional eldercare has increasingly contributed to the repurposing of a wide range of disused buildings, with noteworthy state encouragement. We read this post-reform feature of urban transformation to deploy David Harvey's concept of 'capital switching' in new ways that are emerging in studies of China's urban geography. Harvey's framework has already been used in analysing China's multi-decade boom in the built environment. It exposed the stabilising effects of diverting capital from overaccumulation in single turnover and realisation cycles into deferred returns in the multi-cycle built environment albeit complicated by complex and distinctive interpenetrations of consumption fund and fixed capital, for-profit and not-for-profit sectors, state and non-state interests, and so on. This certainly speaks to the origins of contemporary Chinese urbanisation booms. But repurposing for institutional eldercare now appears to be playing a role in abating mounting overaccumulation and potential devalorisation in the built environment, pairing these unprofitable buildings with for-profit eldercare services operating and yielding profits in repeated single turnover cycles. We experiment with bundled commodity theory to understand repurposing buildings for residential eldercare in this new context.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".