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LITERACY RATE IN PERM GOVERNORATE ON THE DATA FROM THE ALL-RUSSIAN POPULATION CENSUS OF 1920

2023· article· en· W4366407500 on OpenAlexaboutno aff
YULIYA SHUVALOVA

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicNight-time city culture
Canadian institutionsnot available
Fundersnot available
KeywordsCensusDemographyLiteracyQuarter (Canadian coin)GeographyPopulationHuman settlementSocioeconomicsSociologyPedagogy

Abstract

fetched live from OpenAlex

The article considers the level of literacy of the inhabitants of the Perm governorate in 1920. The study is based on the materials of the All-Russian Population Census of 1920. The correlation of literate and illiterate adults aged 20-29, 30-39 and 40-49 is analyzed. For ease of comparison, the number of literate adults is given in absolute terms and in percentage terms in relation to the total number of a particular gender and age group. The calculated data are systematized in the following tables. The general patterns in the distribution of literates are revealed. Residents of cities and factory settlements had more opportunities in the field of education in the first quarter of the twentieth century. The gender identity of illiterates aged 25 to 49 was typical for traditional society: women had fewer opportunities to get an education. Educational opportunities of school-age children did not depend on gender characteristics. The level of literacy was more significant in the age group from 20 to 29 years old. There were more literate women of 20-29 years old in cities, towns and villages in absolute numbers than men of the same age. This indicates a gradual change in attitudes to education in general and to women's education in particular, which was took place in the Russian provinces in the early XX century.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.117
GPT teacher head0.376
Teacher spread0.259 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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