How Does the Concept of Guanxi-circle Contribute to Community Building in Alternative Food Networks? Six Case Studies from China
Bibliographic record
Abstract
As social innovations that help to transition towards a more sustainable food system, alternative food networks (AFNs) in China have attracted much scholarly attention in recent years. However, studies of the community building behavior of AFNs at the micro-level in the Chinese social context are scant. Through in-depth case studies conducted between 2017 and 2021 and social network analysis, our study examines how founders of AFNs successfully facilitate community building among their customers. We find that in China, the traditional social-cultural construct, guanxi, plays a critical role in AFNs’ community formation and expansion. The study identifies a three-stage framework for understanding the community building process of AFNs. First, a group of guanxi of the same kind would form a guanxi-circle. Second, the initial guanxi-circle is enhanced and expanded to multiple secondary guanxi-circles. Third, these multiple guanxi-circles together and the interactions among them constitute the community of AFNs. We argue that to strengthen the community, AFNs operators should inspire key members to form secondary guanxi-circles by enhancing their cognitive trust and emotional trust.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".