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Розвиток білінгвальної освіти в провінції Онтаріо (Канада) наприкінці ХІХ – початку ХХ століття

2013· article· uk· W2923211693 on OpenAlexaboutno aff
Юлія Шийка

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2013
Typearticle
Languageuk
FieldSocial Sciences
TopicEducation, Leadership, and Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Здійснено аналіз розвитку білінгвальної освіти у провінції Онтаріо (Канада). Період дослідження – кінець ХІХ – початок ХХ ст. Вивчено та проаналізовано науково-педагогічну літературу, присвячену висвітленню проблеми розвитку бінінгвального навчання в англомовних країнах, а також досліджено особливості розвитку двомовного навчання у провінції Онтаріо. Встановлено, що білінгвальні школи в Канаді активно розвивалися наприкінці ХІХ і на початку ХХ ст. Выполнен анализ развития билингвального образования в провинции Онтарио (Канада). Период исследования – конец XIX – начало ХХ века. Изучена и проанализирована научно-педагогическая литература, посвященная проблемам развития билингвального образования в англоязычных странах, а также исследованы особенности развития двуязычного обучения в провинции Онтарио. Установлено, что билингвальные школы в Канаде активно развивались в конце XIX – начале ХХ века.The analysis of the development of bilingual education in Ontario, Canada, has been carried out. The period of the investigation is the end of the 19th – the beginning of the 20th century. Scientific pedagogical literature concerning problems of bilingual education development in English speaking countries has been analyzed. Peculiarities of the development of bilingual education in Ontario were examined. It was summarized that bilingual schools in Canada were actively developing at the end of the 19th– the beginning of the 20th 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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0060.011
Scholarly communication0.0010.003
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.003

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 head0.204
GPT teacher head0.443
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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".

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Published2013
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