Evolutionary Game Analysis of Multiple Participation in Source Classification of Domestic Waste
Bibliographic record
Abstract
It’s difficult to guarantee the effect of the source classification of domestic waste only by government, so it’s necessary to rely on the participation of residents, social organizations and other forces. In this paper, by constructing the evolutionary game model of government, residents and social organizations, studies the evolutionary stability strategy, and uses matlab for simulation analysis. The results show that (1) When government loosely supervises and residents don’t participate in the classification, the frequency of participation of social organizations is not high, which is not conducive to the classification of waste sources. (2) When government strictly supervises and residents do not participate in the classification, social organizations are more active in participating, but the government’s management cost is too high. (3) When government loosely supervises and residents participate in classification, the participation rate of social organizations increases, and residents’ awareness of independent participation in waste classification is increased, also can achieve the desired objective waste classification. (4) When government strictly supervises and residents participate in classification, social organizations participate actively at the highest level, this form of positive attitudes by all three parties is also the best way to efficiently carry out waste classification at source.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".