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Record W2283373875 · doi:10.1177/0002764215580585

Social Networks in East and Southeast Asia I

2015· article· en· W2283373875 on OpenAlexaff
Vincent Chua, Barry Wellman

Bibliographic record

VenueAmerican Behavioral Scientist · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGuanxiKinshipContext (archaeology)ChinaSocial capitalSociologySocial network (sociolinguistics)SocialismHierarchyInterpersonal tiesPolitical sciencePolitical economySocial sciencePoliticsGeographyCommunismLaw

Abstract

fetched live from OpenAlex

These articles examine social networks in the context of Asia. Their pages contain numerous examples showcasing the primacy of social context in the patterning, accumulation, role, and value of social networks and social capital. Network characteristics follow from national and institutional characteristics: In China, kinship networks are prominent all throughout the life course. Meanwhile, guanxi continues to be an important factor in the labor market and academic success of Chinese individuals, despite the shift from socialism to capitalism. In Japan, mutual monitoring among kin and coworkers make for a society based on strong ties. In Korea, voluntary associations are important communal spaces for meeting diverse contacts. In China’s neighborhoods, cooperation between neighbors coexists with social control from above to reinforce social hierarchy. The issue ends with a note about the importance of cultivating guanxi in organizations and in everyday life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.066
GPT teacher head0.356
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations30
Published2015
Admission routes1
Has abstractyes

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