Study of Poverty Alleviation Effects for Chinese Fourteen Contiguous Destitute Areas Based on Entropy Method
Bibliographic record
Abstract
China has begun to implement a new round of poverty alleviation and development since 2011, according to the regional distribution, the poverty counties were divided into fourteen destitute areas as the main battlefield in next ten years for China's poverty alleviation. In order to understand the poverty alleviation effects more objectively, this paper uses entropy method to evaluate fourteen contiguous destitute areas in China in 2012, and makes correlation analysis with two reference groups which is one of the Characteristic of this paper. The results show that, poverty alleviation effects of fourteen contiguous destitute areas in 2012 is generally poor, because the mean values of five different correlation degrees in table 3 are lower than 50%, that means the difference between the evaluation value for each area with the minimum reference group does not reach half of the difference between the maximum and minimum reference groups. LiuPan Mountain Area’s performance is the best, the lowest is Wumeng Mountain Area. It is surprising that the performance of Wuling Mountain Area, which is pioneer of regional development and poverty alleviation confirmed by State Council of China, is poor. The comprehensive evaluation value of Wuling Mountain Area is only above the value of Tibet Area and Wumeng Mountain Area. In addition, from the comparison of four first-level indexes, the index of production and life makes the best contribution for comprehensive poverty alleviation effects, followed by index of social development and economic development, and the index of works progress of poverty alleviation is ranked last.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".