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Record W2018444529 · doi:10.5539/jsd.v8n4p112

Process of Economic Terms Internationalization (By Materials of the German, Russian and Tatar Languages)

2015· article· en· W2018444529 on OpenAlexvenueno aff
Alfiya Nailevna Zaripova, Anna D. Fominykh

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage, Communication, and Linguistic Studies
Canadian institutionsnot available
FundersKazan Federal University
KeywordsTatarTerminologyLinguisticsGermanInternationalizationRomanianForeign languageProcess (computing)Political scienceBusinessComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

The paper presents the characteristics of internationalisms in the economic terminology of German, Russian and Tatar languages. The emergence of borrowings in economic terminology is stimulated by the process of globalization, and is the result of cooperation between the countries. Successful achievements in this field are the property of many countries. Borrowings in the economic sphere are international, possible to be allocated into comparable and non-comparable areas. The emergence of non-comparable area in the studied languages is due to cultural and historical conditions of their functioning. In the process of using international vocabulary the issues of semantic inaccuracy and irrational use of words may arise. All borrowed lexemes are subject to formal and functional assimilation in languages. Materials research can be used in the teaching of German, Russian and Tatar languages, as well as in the teaching of subjects such as Terminology, comparative linguistics and lexicology.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.006
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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 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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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