A Study on the Relationship between Social Policy and Social Capital: Evidence from Asian Countries
Bibliographic record
Abstract
There are two competing hypotheses regarding the relationship between social policy and social capital. The crowding-out hypothesis suggests that public resources allocated through social policies and programs eradicate various forms of social capital such as trust, social network and norms. The crowding-in hypothesis, on the contrary, emphasizes virtuous cycles between social policy and social capital. Empirical evidence, mostly conducted for Western countries with advanced economies, has not produced consistent findings regarding the relationship. This study empirically tested these hypotheses based on data from 24 Asian countries. This study further explored how relationships between social policy and social capital vary by sub-regions of Asian countries. Findings from both macro- and micro-level analysis suggest that social policy influences social capital differently according to the forms of social capital. There also seems to be heterogeneity across sub-regions in the relationship between social policy and social capital. Theoretical and policy implications as well as future research directions were further discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".