Socio-economic Impact of Chinese Agribusiness Entrepreneurs in Russian Far East on Local Farmers
Bibliographic record
Abstract
Since the liberalization of the Sino-Soviet border in 1990’s, Chinese farmers have been actively engaged in the economy of Russian Far East. Literature suggests that Chinese workers fill a labour shortage, contribute positively to local food security, with negative impacts being more socially normative and political, than economic. So far no economic empirical research exists about Chinese farmers’ presence in Russian Far East. On the basis of a panel data, an econometric model was used to analyse socio-economic impact of Chinese agribusiness entrepreneurs in Russian Far East on local households. Regression models show that presence of Chinese farmers in Russian Far East increases the probability of higher well-being, farm income, food costs and share of Chinese food purchased among Russian Far East households. These results suggest that benefits of cooperation with Chinese farmers and retailers should not be ignored when designing policies towards sustainable development of rural areas in Russian Far East. Possible environmental, social and economic externalities of further soybean production in Russian Far East are also discussed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".