Communication in times of crisis: Information flow among Chinese hog producers during the African swine fever outbreak
Bibliographic record
Abstract
Abstract The outbreak of African swine fever (ASF) had an enormous economic and social impact on Chinese hog producers. Using a face‐to‐face survey with hog farmers from two regions of China, Chongqing, and Hebei, this research investigated how social influence affects producers’ behavior under disease outbreak using social network analysis. It was analyzed how information flows during an epidemic, such as ASF. Results indicate that hog producers used phone and text more frequently to communicate during the epidemic than before. Face‐to‐face meetings with other hog producers and sales agents decreased during the ASF epidemic—potentially leading to isolation. Moreover, the frequency of face‐to‐face meetings with veterinarians decreased for farmers living in a village in Hebei but remained the same for hog producers in Chongqing. This suggests that the desire to have less face‐to‐face meetings was being replaced with the demand for more help regarding hog health from veterinarians when hog producers lived farther away from each other compared to those living closer together. Employing a random effect ordered probit model, these results were further validated, showing that hog producers dramatically reduced their communication frequency with others after the outbreak of ASF. Findings provide insights into how information flows and how actors communicate during a situation of crisis. [EconLit Citations: D71, D85, Q12, Q18].
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".