Escaping the Curse of Economic Self-interest: An Individual-level Analysis of Public Support for the Welfare State in Japan
Bibliographic record
Abstract
Abstract Despite the general consensus that individualistic utility-optimising behaviour reduces popular support for the welfare state, we still know little about how and to what extent such negative effects of self-interested calculus are mediated by other attitudinal factors, particularly solidaristic values and principles. Using individual-level data from the Japanese General Social Survey, this study seeks not only to qualify existing findings on welfare preference formation but also to explore the hypothesis that the negative impact of economic self-interest is offset or moderated by solidarity-oriented values and beliefs. The author finds that the oft-made claim that material interest and individualistic ideologies undermine welfare support can be replicated in the context of Japan. The results also provide evidence in support of the liberal nationalist contention that popular discourse on welfare is significantly directed by a sense of national unity. Data from Japan also elucidate the fact that a strong sense of social trust significantly weakens the salience of self-oriented cost–benefit calculations. These findings suggest that solidarity-related variables such as national identity and interpersonal trustworthiness should receive more attention in future research on welfare attitudes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".