Literacy of the Population of the Ural at the End of the 19th — First Quarter of the 20th Century (on the Materials of the Ekaterinburgsky Uyezd of the Perm Governorate and the Sverdlovsky Okrug of the Ural Oblast)
Bibliographic record
Abstract
In Russia in the late 19th — early 20th century, there was an acute issue of increasing the literacy rate of the population, leading to the development of state measures to improve it. In various regions, the situation with increasing the literacy rate developed in different ways, including in the Ural region. When comparing literacy rates over time, it is necessary to take into account changes in administrative boundaries. The Sverdlovsky okrug as a part of the Ural oblast in 1926 was territorially larger than the Ekaterinburgsky uyezd of the Perm governorate in 1897; it included parts of the Krasnoufimsky and Irbitsky uyezds. Therefore, in this study, the literacy rate of the population was calculated for the three indicated uyezds simultaneously, and the results were compared with the indicators of the Sverdlovsky okrug. Research findings revealed a substantial growth in the literacy of the population. The literacy rate has increased more significantly among the rural population than among the urban population, and among women than among men. However, the literacy gap between them still persisted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".