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Social capital and its relevance to the Japanese‐model welfare society

2004· article· en· W2103419395 on OpenAlexaff
Raymond K. H. Chan, Chau‐kiu Cheung, Ito Peng

Bibliographic record

VenueInternational Journal of Social Welfare · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial capitalExperiential learningWelfareSocial mobilityRelevance (law)Individual capitalSocial WelfareSociologyEconomicsEconomic growthPublic economicsFinancial capitalPolitical scienceHuman capitalSocial scienceMarket economyLaw

Abstract

fetched live from OpenAlex

Current debates and initiatives relating to the welfare regime in Japan focus on the contributions of informal and community networks. In this article, we adopt the concept of social capital, which is assessed according to three categories – structural social capital, experiential individual social capital and anticipatory individual social capital – in order to evaluate the assumptions and strengths of community in Japan. The findings are based on a small‐scale survey conducted in the Greater Kobe area in 2002. The study revealed that the level of structural social capital is ‘average’ and the level of experiential individual social capital is ‘rather low’. However, the anticipatory individual social capital, which is the expectation of future assistance whether conditional or unconditional, is higher than the experiential individual social capital. The findings suggest that, in Japan, people's belief that they will receive assistance in the future has a significant impact on their level of achievement. Such findings may help us understand the nature of the welfare regime in Japan: it depends on a sense of general trust, which effectively supports the informal groups and community networks that provide assistance to their members.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.311
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2004
Admission routes1
Has abstractyes

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