Bibliographic record
Abstract
The method of game theory was applied to explore the game relation among all the parties involved in the course of returning farmland to lake and in immigrant resettlement which chose the Dongting Lake Region as an example.The Dongting Lake region locates at the south of Jing river,middle reaches of Yangtze River,the north of Hunan Province.It is a huge alluvial plain around the Dongting Lake,which total area is 18 780 km2,and 15 200 km2 in the Hunan Province accounting for 80.9% of total area,3 580 km2 in Hubei Province accounting for 19.1% of total area.It is the commodity grain base and industry material base in China and possesses important economical station,but the Dongting Lake Region is also affected by very frequent and serious flood-waterloggy disaster in China.The results showed that the profits of local governments and peasants must be considered in the project of returning farmland to lake and the work of immigrant resettlement,the action motive of each game party must be sought in terms of Economic Rational People,and appropriate policy must be established on the basis of game analysis.The returning farmland to lake engineering would not be brought to success only depending on the local government investment,the investment from the center government was necessary and served for a dominant function.But the most optimum distribution of resovces would not be achived only depending on the center government investment.The center government,or the superior government,should consider its ecological benefit of flood prevention and economic benefit of the local governments and the peasants in a comprehensive way.To develop local economic and enhance the enthusiasm of the local government admitting the immigrant through implementing are long and steady good policy,so the project of returning farmland to lake could be carried on in proper sequence.It is not only unreasonable to increase the probability that the local governments admit the immigrant through reducing the cost of immigrant resettlement,but also damaging the benefit of the immigrant.In order to enhance the enthusiasm of the local government admitting the immigrant,the superior governments should consider the benefit of local governments,carry out the favourable policy continuously and stably,and dismiss all doublts of the local government.Those are the ultimate way to solve the immigrant problems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".