Bibliographic record
Abstract
After a decade of privatization, Black Earth villagers faced dwindling opportunities to gain access to the land that was rightfully theirs. In addition to the bureaucratic obstacles that stood in the way of land distribution, rural people faced a hostile economic environment and a local political landscape that prevented them from profiting from ownership. On most farms, labor payments and membership entitlements diminished over time, and ownership-based incentives were minimal. Amidst deepening poverty, villagers saw their chances ever of acquiring land or making a decent livelihood recede into the distance. Farming land required start-up capital, and villagers had few ways to get it. Wegren et al. write, “While it was hardly the intent of market reforms to impoverish millions of rural Russians, this is exactly what has happened.” That the future held few prospects for most rural people became clear early in the process. In 1995, A. Rud'ko, a Kharkiv pensioner, expressed a common sentiment when he observed in a letter to the regional newspaper that “now, no honest villager can afford to buy land for himself, much less a combine or tractor. And without machinery, what can be grown today?” For most farms, amidst continuing political uncertainty, chances for capital investment from within or outside of the Black Earth countryside were slim. Employment outside of reorganized collectives was scarce.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".