Bibliographic record
Abstract
Study of the state's grain collection policy during the years of the Great Patriotic war remains an urgent task for historians studying the functioning of Soviet economy during the war period.This article shows how harvesting and grain collection campaigns took place in 1941 in one of the main grain-producing regions of Western Siberia -Novosibirsk region.As a result of military mobilization, region's agriculture lacked manpower (a special problem was mobilization of skilled workers in the army) and machinery.Lack of machinery led to increase of the share of manual labor in the total volume of agricultural work.Despite the fact that the state urgently needed Siberian bread, the grain collection plan for the Novosibirsk region in 1941 turned out to be lower than that of 1940.This was due, firstly, to the limited capabilities of MTS, which affected the decrease in volumes payment in kind.Secondly, in 1941 the state lowered the regional annual norms for compulsory grain supplies for the region, which, coupled with a reduction in the taxable supply of land of collective farms, affected the size of the grain collection plan.Grain collection campaign of 1940, carried out in accordance with the new hectare principle of calculating compulsory deliveries, was devastating for the Novosibirsk region.Weakening of the grain procurement "press" in 1941 was probably due to the need to preserve agricultural production potential of the Novosibirsk Region, which was undermined by the excessive seizure of grain in the previous year.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".