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Record W2990098419 · doi:10.32405/2617-3107-2019-1-6

BILINGUALISM AS A PEDAGOGICAL PROBLEM IN THE USA AND CANADA

2019· article· en· W2990098419 on OpenAlexaboutno aff
Nina Nikolska

Bibliographic record

VenueEducation Modern Discourses · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroscience of multilingualismBilingual educationForeign languageCompetence (human resources)Process (computing)Realization (probability)LinguisticsMathematics educationSubject (documents)PedagogyComputer scienceSociologyPsychologyMathematicsLibrary science

Abstract

fetched live from OpenAlex

In this article the author analyzed the content of the terms “bilingual education” and “bilingual”. The author claims that in pedagogical sources quite often these two concepts are used. The analysis of these two areas and their main features is carried out, the author considers it necessary to distinguish between these two concepts: bilingual education (bilingual, bicultural education) is a process, the realization of which is based on the use of two programs with the aim of mastering subjects that include cultures, from which these two languages occur. Bilingual education is a means of obtaining education using two languages as a means of teaching, in the process of which the person formation open to interaction with the outside world takes place. Since in the bilingual education non-native or foreign language process is viewed not only as a means of everyday communication, but also as the world knowledge instrument of special knowledge, as a result pupils achieve the linguistic and subject competence high integrative level. Teaching using two languages is quite common in the USA and Canada.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0270.007
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.313
Teacher spread0.275 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

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