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PRIMARY PUBLIC SCHOOL IN THE RUSSIAN EMPIRE IN THE LAST QUARTER OF THE XIX CENTURY AS A VECTOR OF SPIRITUAL AND MORAL EDUCATION

2017· article· en· W2775989059 on OpenAlexaboutno aff
F. F. Gumerova, G. M. SIBAEVA

Bibliographic record

VenueHistorical and social-educational ideas · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)LiteracyQuarter (Canadian coin)Primary educationPedagogyPopulationClass (philosophy)SociologyMathematics educationPsychologyPolitical scienceLawHistory

Abstract

fetched live from OpenAlex

The article is dedicated to the popular schooling reform which touched upon Russian elementary schools. The appearance of the elementary schools (among them ‒ elementary people’s schools, rural schools, reading and writing schools, labor schools, church schools, Sunday schools) caused the increase of the literacy of the children who belonged to the low class population. Now they had a good chance to learn reading and writing and to get some elementary scientific knowledge. The children also learnt the church subjects: Religion, the Sacred History, and Church Music. They developed the moral qualities in children. Except schools, which the children of the low class visited, the teachers’ schools were created in Russia where the specialists for the work in these schools were trained. The author of the article also concerns the importance of the county which financed the elementary schools and took part in the organization of these schools. The church and Sunday schools were created at the expense of the local church communities. They were under control of the Orthodox Department. But the pupils of these schools taught both church and general subjects.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.046
GPT teacher head0.338
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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