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Record W2982495138 · doi:10.5430/ijhe.v8n7p39

To the Question of the Interference and Positive Transfer When Teaching Russian to Hispanic Students

2019· article· en· W2982495138 on OpenAlexvenueno aff
Dinara R. Valeeva, Alina Ershova

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency Medicine Education and Research
Canadian institutionsnot available
FundersKazan Federal University
KeywordsLinguisticsVariety (cybernetics)Relevance (law)Process (computing)Computer scienceContrast (vision)First languageRussian languagePsychologyMathematics educationArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

The real research is devoted to problems of an interlingual interference in the process of studying of Russian by students of the Hispanic countries. The relevance of the article is caused by the fact that at all variety of the scientific works devoted to the contrast description of various languages and the analysis of their interference, it is a little work comparing the Russian and Spanish languages. The research was conducted for the purpose of comparison of the separate language phenomena of the Russian and Spanish languages and the description of those lexical and grammatical features which knowledge will allow teachers to warn and correct native speaker errors in the Russian speech, therefore, to optimize training process. According to the authors, it is advisable to use the national focused tests and tasks on classes in RKI and also governed with explanations and examples in the native language of students that will increase their intellectual activity.

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)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.018
GPT teacher head0.415
Teacher spread0.398 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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