Formation of the Orthodox Content of Education in the Last Quarter of the XVII Century: Special Educational Literature
Bibliographic record
Abstract
e aim of the article is to grasp the mental division of mind of Russian intellectuals in solving the problem of organizing Orthodox school education. e organized schooling was largely absent in Muscovy and the practice of studying with a private teacher substituted for schools. e idea to create Orthodox schools, di erent from Latin ones of Western Europe, was set forth in the last quarter of the XVII century and led to the compilation of several handwritten teachers' collections, the compilers of which sought to present in them the content of school Orthodox education. at gives the ground for comparison between two of them: ABC for kids by Evfimy from Chudov monastery and School ABC by Prokhor Kolomniatin. Conclusions: two di erent compilers, who used di erent texts, show similar thematic composition in constructing a school curriculum. It includes: 1. Grammar as a main section; 2. Explanation the necessity of study and the meaning of “Wisdom”; 3. Norms of the pupils' conduct; 4. Instructions on piety as well as threats for ignoring them; 5. Catechisms and the texts of prayers; 6. Instructions for teachers. e two authorcompilers nearly exhausted the possibilities of the Muscovite repertoire of texts, which could be used for children's education. However, they added some new translations as well as their own compositions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".