Economic Analysis of Frequent Disasters in Chinese Coal-mining Enterprises
Bibliographic record
Abstract
Disasters occurred frequently in Chinese coal-mining enterprises has attracted significant coverage from the whole society. Employer will invest in safety and health improvement until the cost is more than the expense of paying higher wages, compensation of employees’ death and other accident and illness costs. Thus, market price of product, rate of worker’ wage and government regulation are highly imperfect alternatives concerning the reduction of disasters in coal and mine enterprises. As a result, continued reliance on all three approaches, a rational mechanism among government, employee and employer should be established. Key Words: Disasters in coal-mining enterprises; Market price of product; Rate of workers’ wage; Government regulation cost Resume Les desastres frequentes dans les mines chinoises ont attire l’attention de tous les milieux sociaux. Les employeurs ne pourront investir pour l’amelioration de la securite et de la sante qu’au moment ou le cout est largement superieur a la depense du saleire, a la compensation de la sante des employes et a d’autres couts d’accidents et de maladies. Pourtant, le prix du marche des produits, le taux du salaires des employes et a la regulation gouvernementale sont imparfaits dans les interventions de l’amelioration de l’environnement. Pour reduires les desastres des mines, il faut trois solutions de regulation pour creer un systeme de fonctionnement logique. Mots-cles: Les desastres dans les mines chinoises, le prix du marche des produits, le taux du salaires des employes, le cout de la regulation gouvernementale 摘 要 佔據中國能源重要地位的礦業企業內頻繁發生的礦難引起社會各界的關注。只有對工人的補償大於改善環境的支付時，礦主才會為改善生產環境安全狀況投資。文章通過對影響礦主行為的產品市場價格、工人工作率和政府規製成本的經濟學分析，闡述了礦難屢禁不止的原因。產品市場價格、工人工作率和政府規製成本在改善安全環境方面的作用，都是有缺陷的。要減少礦難的發生，必須綜合運用三種調節方式，建立合理的運行機制。 關鍵詞：礦難；產品市場價格；工人工資率；政府規製成本
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".