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Record W1982520167 · doi:10.1177/0002764215580615

Can Family Financial Resources Buy Friends?

2015· article· en· W1982520167 on OpenAlexaff
Eric Fong, Yuying Tong

Bibliographic record

VenueAmerican Behavioral Scientist · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFriendshipSocioeconomic statusChinaDemographic economicsGeographySocioeconomicsEconomic growthSociologyDemographyEconomicsPsychologySocial psychologyPopulation

Abstract

fetched live from OpenAlex

This study examines empirically the relationship between the economic conditions back home and the friendship patterns of migrants at the new destination. The migrant adjustment process is not just associated with the individual socioeconomic status, but is also related to family economic conditions in their place of origin. Our study, based on data collected in 2010 on migrant workers in seven cities located in Pearl River Delta and Yangzi River Delta in China, confirms the relationship. Our findings indicate that rural migrants in China with higher income and better economic conditions back home are associated with more friends in the host society. We also find friendship patterns to be different between male and female migrant workers. In particular, family economic conditions back home are particularly important for males’ friendship pattern, but not for females. Implications of the findings are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.340
Teacher spread0.305 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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