Changes in publication languages and citation practices and their effect on the scientific impact of <scp>R</scp>ussian science (1993–2010)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".