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Record W3123012104

Trust, Reciprocity, and Guanxi in China: An Experimental Investigation

2012· preprint· en· W3123012104 on OpenAlexaff
Fei Song, Charles Bram Cadsby, Yunyun Bi

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsUniversity of GuelphToronto Metropolitan University
Fundersnot available
KeywordsGuanxiReciprocity (cultural anthropology)Social psychologyPsychologyInterpersonal communicationSocial distanceAffect (linguistics)Social trustPopulationChinaSalientCognitionSociologyPolitical scienceSocial capitalDemography
DOInot available

Abstract

fetched live from OpenAlex

Employing a financially salient one-shot trust game, we examined the influence of guanxi (social distance) on trust and reciprocity dynamics in China. The results demonstrate the commonly-accepted negative relationship between trust and social distance, whereas reciprocity is less responsive to the change in guanxi connections. The significance of trust and reciprocity in social life has long been recognized by various social sciences: anthropology, psychology, economics, sociology, and business studies. Across disciplines there is a consensus that trust and reciprocity as forms of social capital are critical to our society, as noted by Buchan, Croson, and Johnson (forthcoming): “Trust and reciprocity are integral elements in economic transitions between companies, customers and retailers, between employees and employers, as well as in determining economic performance. ” Nobel Laureate Kenneth Arrow (1972) in "Gifts and Exchanges" cogently remarked: “Virtually every commercial transaction has within itself an element of trust, certainly any transaction conducted over a period of time. It can plausibly be argued that much of the economic backwardness in the world can be explained by the lack of mutual confidence. ” The underlying logic of this statement is the following: in high-trust societies, individuals need to spend fewer resources to protect

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.060
GPT teacher head0.368
Teacher spread0.308 · 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 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

Citations10
Published2012
Admission routes1
Has abstractyes

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