Guanxi in an age of digitalization: toward assortation and value homophily in new tie-formation
Bibliographic record
Abstract
Abstract How do people form personal ties? A consensus holds in sociological and social network scholarship that in-person networks are dominated by status homophily and that guanxi networks rely extensively on balance. This article argues that social networking sites (SNSs) reconceptualize the character of homophily and tie-formation altogether in guanxi networks. Drawing on 50 semi-structured interviews with Hong Kong youth from 2017 to 2020, this article examines how the technical capabilities of SNSs and principles of guanxi culture come together to erode status boundaries, create access to larger networks, and cause spillovers of information and tie strength. As a result, the basis of tie-formation in guanxi networks on SNSs shifts from balance to assortation and status homophily to value homophily. In this transformed calculus of tie-formation, two typologies of values rise to the fore: substantive values that reflect opinions and interests, as well as structural values that reflect networkability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, not a consensus.
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".