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Record W3199389161 · doi:10.18662/lumproc/atee2020/29

Main Principles of Using Audiovisual Method in Teaching the Native Language to Children of Pre-School Age in the Ukrainian Diaspora of The USA and Canada

2021· article· en· W3199389161 on OpenAlexaboutno aff
Iryna Rudnytska-Yuriichuk

Bibliographic record

VenueLumen Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUkrainian Cultural and Linguistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianDiasporaPersonalityConsciousnessTask (project management)Process (computing)PedagogyPsychologySociologyMathematics educationPolitical scienceComputer scienceLinguisticsGender studiesEngineeringSocial psychology

Abstract

fetched live from OpenAlex

In the national educational system of the Ukrainian diaspora of the USA and Canada the pre-school period covers the first stages of extra-familiar education, where establishing of child’s consciousness and connecting to spiritual values of the Ukrainian nation are taking place. Efficiency of this process depends on multiple factors. A significant role among them is played by didactic provision of educational-instructional process in pre-school educational institutions of various kinds whose main aim is to form national consciousness of the pupils through acquiring the Ukrainian language, as well as mastering contents of Ukrainian Studies subjects. Pedagogues at Ukrainian pre-school institutions in diaspora conditions clearly understand that the task of bringing up a child before the age of 6 implies providing them with various, beneficial for growing and useful for them, qualities. That is why teachers contribute to children acquiring such knowledge, abilities and skills which would help them to successfully prepare for elementary school in the future. Since the main task of Ukrainian pre-school education lies in development of a child’s personality by means of Ukrainian Culture studies, a pedagogue (teacher) has to know Ukrainian and all subjects well.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.338
Teacher spread0.310 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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