“Kill the chicken to scare the monkey”: Heavy penalties, excessive <scp>COVID</scp>‐19 control mechanisms, and legal consciousness in China
Bibliographic record
Abstract
Abstract This study analyses the legal consciousness of Chinese citizens during the COVID‐19 pandemic when the authoritarian state invoked heavy penalties to deter noncompliance with its excessive COVID‐19 restrictions. China used the approach of “killing the chicken to scare the monkey,” publicly punishing those who violated restrictions in order to deter noncompliance. This article explains why ordinary citizens supported this selective application of the law, as well as how the possibility of being the “chicken” contributed to their compliance (or noncompliance) with excessive COVID‐19 restrictions. It suggests that the uncertainty and unpredictability of law in the authoritarian state bred fear, which then led to compliance, regardless of the lack of procedural fairness. People's dissatisfaction with the rules, however, led them to tolerate and even support the noncompliance of people they trusted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".