Renegotiating Social Risks in the People’s Republic of China and Japan
Bibliographic record
Abstract
There have been various attempts to capture the direction of welfare provision in Japan and the People’s Republic of China (PRC) as a regionally coherent welfare regime, following on from attempts by the welfare regime literature to categorize nation-states by the characteristics of their welfare provision in the West. However, stark differences between the PRC and Japan as regional neighbours, and even within the regions of each country, pose a challenge to this kind of macro-level theorizing. This special issue seeks to supplement macro perspectives on welfare regimes by exploring a range of welfare policies across both states from an ethnographic, bottom-up perspective, which captures the dynamic nature of welfare and highlights the importance of understanding how local actors request, interpret, and implement risk management strategies. The management of social risks is shown not to have one clear direction determined by, for example, market logic: instead, this special issue highlights the ways in which the burden of risk shifts between family, market, state, and communities unevenly over time, reflecting underlying institutional norms which are always up for negotiation. In doing so, this special issue emphasizes the importance of local, contextualized understandings of welfare, and suggests that the comparative welfare regimes literature should seek the micro-institutional foundations of welfare provision as the basis for comparison.
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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 | 0.002 | 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 | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".