Bibliographic record
Abstract
The basic question considered in the publication the difference of influences of the tutors and the governesses representing the different countries in Russia, and also the gender aspects of noble children education. Right up till the XXth century the education as a whole and the women education exerted influence upon politics. When in the XVIIIth century the requirements and the attitude to women had changed, the new models of education which always in Russia was gender focused appeared, that helped to structure the appropriate gender identity. The presence of the tutor or governesses provided teaching the good manners, mastering in perfection with a foreign language and education as a whole. France for Russia of the XlXth century was considered as the role model, the presence of the French governess was an attribute of a good form though the French were considered as thoughtless. English tutors differed from others by their good manners, severity and moreover they taught not obligatory and not fashionable at that time English language that testified to a high level of family's ambitions. German tutors were appreciated in merchant and military families due to their accuracy, pedantry and high organization. The governesses not only formed the children's outlook, but also were capable to change all Russian traditional family way, that subsequently has affected on forming of a new women generation which had been educated in norms of practicalness. In the 50-70 of the XIXth century this generation received the european education, that in the last quarter of the XlXth had an effect on development of Russian emancipation processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".