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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".