Bibliographic record
Abstract
The article thoroughly investigates the polarization processes started in agriculture and rural areas. New market environment increased the division of labour within territories and pushed agriculture to the ample resource south and the suburbs. The Russian agriculture overcomes the crisis in certain areas with more favorable natural environment and districts still having vital demographic statistics and work force. Besides, the article focuses on the ways the government and other bodies may tackle the agricultural challenges in the whole country, within a region, a provincial administrative area, and a rural settlement. Present-day challenges of previously developed areas in the North are deeply rooted into the Soviet extensive development psychology, inability to see the existing natural, social and economical limitations for a long period of time. Non-black soil areas witness a considerable difference between the suburbs and provinces. The Kostroma Region may serve a typical example. As soon as in the late 20th century rural population and commercial farming was preserved in its suburban area only. The 3, 4% of the territory in the Kostroma Region concentrates 19% of its population, 16% of cattle and a quarter of gross agricultural output. White straw crops and livestock efficiency index is 2-3 times higher than in the province. The suburb adjoining areas also boost excellent prospects. However, the province witnesses an increase in depopulation and decline in farming industry, and a shift towards forestry or any other industry is not possible due to the lack of work force. Manturovsky District in the Kostroma Region is a non-black soil provincial area. The district still has 9 agro-industrial plants, and only two or three of them have a chance to survive if greatly reduced. Personal subsidiary farms grow away due to ageing of population and youth migration from rural areas. Forest management is the main activity of the district, but its development is limited by forest resources deterioration and low demographic capacity. The fact that the Ugric rural settlement on the shore of the Unzha river is situated at the distance of 35 km from the railway and from Manturovo city determined its mainly agricultural profile in the past. Only 15% of its population remained, if compared to the early 20th century, 1/10 of the field was sowed in 2007 and the remained lands are running wild. With such depopulation troubles, transition from farming to other activities becomes a rather challenging move and is possible only in the heart of the settlement. The opportunities are limited not only by quantity but also by quality of human capital assets, due to a negative social selection of population having taken place for a long time. Besides managing personal subsidiary farms and public sector employment, activity diversification is narrowed to gathering wild plants and fishing. Instead of development, such places experience devolution, also accompanied by social restrictions. With depopulation and economic crisis, globalization intensified devolution of remote places, encouraging the remaining young people to join the world processes and leave their home villages. It made purely economic criteria submerged by a problem of social environment matching the needs of the youth. Such districts may develop only with extra financial and labor inputs. Justly, rare economic growing-points are related to migrants. Turning remote summer villages into the outskirts of magacities is one of the most essential ways of development for such districts. Almost a half of the privately owned plots in the Ugric rural settlement belongs to town dwellers, mainly to Moscow residents. Town dwellers partially save villages or separate houses, but not extensive farming lands and not rural community. However, with some village population going away, life of summer residents may turn rather hard. Incursions of nature to agropogenic and cultural landscapes are inevitable in such areas as Manturovsky District and field abandoning reduces developed lands to pinpoint spots. Efforts made by federal authorities in the context of national projects and programs, aimed at saving farming in such districts, bring about a pinpoint effect, as merely give assistance to separate plants or farms, and Svoboda, the Ugric production cooperative farm, did not enjoy governmental attention. The Ugric settlement also did not receive livestock efficiency subsidy from regional government, as the cooperative farm had failed to increase annual milk yield more than 1500 kg per cow. Regional and municipal authorities can not afford to bear all the necessary expenses due to the lack of regional tax basis, and the adoption of Federal Law No. 131 deteriorated the situation greatly. Only towns having beneficial geographic and transport location and suburbs of regional centers remain growing points in remote previously developed areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".