A Multidimensional Analysis of Linguistic Variation in Russian and British Newspaper Editorials
Bibliographic record
Abstract
This study conducts a multidimensional analysis of Russian English to examine register variation. It explores Russian English as it appears in the editorial and opinion sections of prominent newspapers. The corpus comprises 1,250 editorials (250 from each of five newspapers), each ranging from 1,000 to 1100 words long. The corpus was carefully annotated for 67 linguistic features using the freely available Multidimensional Analysis Tagger (Nini, 2019). We employed factor analysis within Biber’s (1988) multidimensional framework to identify and interpret significant patterns of linguistic co-occurrence. These dimensions help reveal stylistic and rhetorical choices in different editorial contexts. The findings contribute to understanding non-native English varieties, particularly how Russian English reflects local and global influences. This study sheds light on how English adapts and evolves in diverse linguistic and cultural settings, reinforcing its nature as a pluralistic, global language.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".