The Transformation of Trust in Chinaâs Alternative Food Networks: Disruption, Reconstruction, and Development
Bibliographic record
Abstract
Food safety issues in China have received much scholarly attention, yet few studies systematically examined this matter through the lens of trust. More importantly, little is known about the transformation of different types of trust in the dynamic process of food production, provision, and consumption. We consider trust as an evolving interdependent relationship between different actors. We used the Beijing County Fair, a prominent ecological farmers’ market in China, as an example to examine the transformation of trust in China’s alternative food networks. We argue that although there has been a disruption of institutional trust among the general public since 2008 when the melamine-tainted milk scandal broke out, reconstruction of individual trust and development of organizational trust have been observed, along with the emergence and increasing popularity of alternative food networks. Based on more than six months of fieldwork on the emerging ecological agriculture sector in 13 provinces across China as well as monitoring of online discussions and posts, we analyze how various social factors—including but not limited to direct and indirect reciprocity, information, endogenous institutions, and altruism—have simultaneously contributed to the transformation of trust in China’s alternative food networks. The findings not only complement current social theories of trust, but also highlight an important yet understudied phenomenon whereby informal social mechanisms have been partially substituting for formal institutions and gradually have been building trust against the backdrop of the food safety crisis in China.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".