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Record W2015995283 · doi:10.1002/asi.22642

Changes in publication languages and citation practices and their effect on the scientific impact of <scp>R</scp>ussian science (1993–2010)

2012· article· en· W2015995283 on OpenAlexaff
Olessia Kirchik, Yves Gingras, Vincent Larivière

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsInternationalizationPromotion (chess)Foreign languageCitationVisibilityImpact factorScientific communicationWeb of scienceComputer scienceProfit (economics)Library sciencePolitical scienceBusinessMathematics educationPsychologyInternational tradeEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

This article analyzes the effects of publication language on the international scientific visibility of R ussia using the W eb of S cience ( WoS ). Like other developing and transition countries, it is subject to a growing pressure to “internationalize” its scientific activities, which primarily means a shift to English as a language of scientific communication. But to what extent does the transition to E nglish improve the impact of research? The case of R ussia is of interest in this respect as the existence of many combinations of national journals and languages of publications (namely, R ussian and E nglish, including translated journals) provide a kind of natural I experiment to test the effects of language and publisher's country on the international visibility of research through citations as well as on the referencing practices of authors. Our analysis points to the conclusion that the production of original E nglish‐language papers in foreign journals is a more efficient strategy of internationalization than the mere translation of domestic journals. If the objective of a country is to maximize the international visibility of its scientific work, then the efforts should go into the promotion of publication in reputed E nglish‐language journals to profit from the added effect provided by the M atthew effect of these venues.

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)0.049
metaresearch head score (Gemma)0.095
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0490.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0140.090
Science and technology studies0.0010.006
Scholarly communication0.0020.005
Open science0.0010.000
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 head0.186
GPT teacher head0.509
Teacher spread0.323 · 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 designObservational
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

Citations62
Published2012
Admission routes1
Has abstractyes

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