Economic impact of poverty alleviation policies in Gansu province, China: An input-output analysis
Bibliographic record
Abstract
In accordance with the United Nations Millennium Development Goals, the Chinese government aims to eradicate poverty, in particular, in the central and western regions by 2020. The primary goal of the 13th Five-Year Plan of the nation is also to eliminate poverty by 2020. To achieve this goal, the government of Gansu Province has introduced a series of development plans and policies, focusing on agricultural development through an increase in the supply of agricultural products and expanding the scale of industries that have regional advantages. One such policy of the local government aims to increase the industrial output of vegetables, fruits, meat, milk and dairy products along with an increase in the number of cattle, sheep and goats. The current study uses the Gansu input-output (I-O) model to evaluate the economic impacts of various agriculture-based policy scenarios - apple, cattle, sheep and goats, meat, and milk and dairy products as stated in the provincial 13th Five-Year Plan. It also analyzes the impact of different consumption expenditure patterns and direct cash transfer to poor households. In order to estimate the impact of the various policy scenarios, the original highly aggregated 2012 Gansu I-O table was disaggregated by expending the number of agricultural sectors and food processing and manufacturing sectors. The impact on output, GDP, and employment calculated in this study offer a basis for decisions related to the sector priorities in regional and rural development. Results indicate that for poverty alleviation and sustainable development of agriculture, Gansu Province should use its regional advantages of developing the apple sector, animal husbandry sector and its related industries, such as the meat sector and milk and dairy product sector
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".