Common ground across globalized English varieties: A multivariate exploration of mental predicates in World Englishes
Bibliographic record
Abstract
Abstract This study tests for similarities and differences in the uses of near-synonymous mental predicates by speakers of different ENL and ESL speech communities to capture whether, and if so to what degree, speakers of different first and second language English varieties use the four near-synonymous predicates semantically differently. Specifically, we focus onI believe, I think, I supposeandI guessin eight native and second-language varieties of English (i.e. American, British, Canadian, Irish, Hong Kong, Indian, Singapore and New Zealand). We adopt a multivariate modeling approach to analyze mental predicates annotated for six semantic variables (verifiability, epistemic mode, epistemic class, epistemic type, evaluation and negotiability) as well as genre. Our findings show the usefulness of exploring Englishes through the lens of semantic structure. Although, on the surface, two groups of English varieties emerge with different preferential patterns of predicates (British, Indian, Irish and Singapore vs. Canadian, Hong Kong and American), at a more abstract level, those predicates share similar semantic combinatory patterns common to all varieties in focus. It emerges that modeling the development of Englishes based on theoretical frameworks that account for simultaneous development of generic (i.e. common to all Englishes) and specialized (i.e. specific to individual Englishes) linguistic patterns may be beneficial. At a time when English has become a worldwide language shaped by globalization, the present study adds to the discussion on the developmental pathways that characterize the evolution of non-native Englishes in the twenty-first century.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".