A Study of Law-Based Chinese Petition System From the Perspective of Evolutionary Game Model
Bibliographic record
Abstract
Petition system (named Xinfang in Chinese) is a typical Chinese system for citizens to express opinions and seek non-lawsuit remedies. It was originally positioned with the emphasis on expression of public opinions, but the general public expect more on its rights relief function. Over the recent 30 years, China’s economy entered into a period of high-speed development, and the redressal of social interest structure aggregated the conflicts of functional position of petition, which has surged the volume of petition letters and visits. The frequent occurrence of social contradictions resulting from blockage of petitions has seriously impaired social stability. Hence, it is pressing to readjust the functional position of petition and guide the system onto the legal track. In this paper, theoretical analysis is made over the strategy selection and dynamic game of both players of petition during the interaction process by building an evolutionary game model, to conclude an ideal state of stable equilibrium. With the theory base, suggestions are proposed on guiding petition onto the legal track.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".