Analysis of Differences in Average Net Income of Peasants in Shandong Province Based on Moran’ I and LISA
Bibliographic record
Abstract
Through relevant analysis of the changing space in the peasants’ average net income from 2005 to 2006 in 139 counties, towns and regions, this thesis works out the overall space statistic index Moran’s I and partial statistic index LISA, which well demonstrates the change of net income made by peasants in this area, and obviously manifest disharmony and difference from one region to another, especially from the east to the west. To solve the problem of big gap between the east and west, more efforts should be made to accelerate the development of the west. Regions in the middle and the west should renew developmental concept, rely on, serve and interconnect with central cities, speed up the transition of the surplus labor, put more investment into the construction of the agricultural infrastructure, strive to develop cooperative economic organizations and agricultural leading enterprises, energetically push the industrialization of agriculture. Key words: average net income of peasants; relationship of space; Moran’s I; LISA; regional economic differences Resume: En faisant des analyses sur la correlation spaciale de la variation des revenus moyens des paysans entre 2005-2006 dans 139 districts et villes dans la province du Shangdong, l’auteur a obtenu l’indice statistique de l’ensemble spacial- Moran’I et l’indice statistique de localite regionale-LISA. Ces deux indices ont bien demontre les variations des revenus moyens des paysans dans tous les districts et villes de la province du Shangdong, les disparates et les ecarts entre les regions et surtout entre l’est et l’ouest. Pour resoudre le probleme d’un ecart trop important entre l’est et l’ouest, il faut accelerer le developpement des regions dans l’ouest. Les regions du centre et de l’ouest doivent renouveler leur conception du developpement et essayer de s’appuyer sur les villes centrales, les servir et les aboucher pour transferer les mains-d’oeuvre surplus, investir plus pour les infrasturctures de l’agriculture, soutenir les organisations economiques de cooperation dans la campagne et les entreprises leaders agricoles et promouvoir l’industrialisation de l’agriculture. Mots-cles: revenus moyens des paysans ; correlation spaciale ; Moran’I, LISA ; difference de l’economie regionale 摘要：通過對山東省139個縣市區2005-2006年農民人均純收入變化的空間自相關分析，得出總體空間統計指數Moran’sI與局部統計指標LISA，這兩個指數在描述山東省各縣市區農民人均純收入變化時具有顯著作用，並明顯體現出區域之間的不協調性和差異性，尤其是東西部差距明顯。解決東西部差距過大問題，需要重點加快西部地區發展。中西部地區要進一步更新發展理念，依託、服務、對接中心城市，加快剩餘勞動力轉移，加大對農業基礎設施的投入，大力發展農村合作經濟組織和農業龍頭企業，積極推進農業產業化經營。 關鍵字：農民人均純收入；空間相關性；Moran’s I；LISA；區域經濟差異
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".