General-criminal prisons of the Russian Empire in the XIX century (on the example of the Orenburg province)
Bibliographic record
Abstract
The paper attempts to provide, on the basis of archival and published materials, a brief description of the state of general prisons in the Russian Empire in the 19th century on the example of the Orenburg province. In the first half of the XIX century, many prison buildings were in a dilapidated state, most of them were wooden. The prisoners suffered from overcrowding, they were not separated by sex and age, the sick were kept together with the healthy ones, they were hungry, they lived in begging. Very often the premises for prisons were private rental houses. There were no medical personnel in prisons, there were epidemics that led to a huge increase in mortality. As for the work, in the first half of the XIX century in prison locks and guards it was introduced in the rarest cases, since there were no special rooms for this. In the post-reform period, many prison premises were repaired, premises began to be rented for hospitals, the prisoners diet improved in the 1980s. The payment for arrest labor was introduced, the educational activity in prisons improved. Despite the measures taken by the government, the state of ordinary prisons in the southern Urals throughout the XIX century was still deplorable due to the fact that there was not enough money, or the local administration was not interested in improving the situation of the prisoners and the state of the prisons themselves.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".