Eh Across Englishes: A Corpus-Pragmatic Analysis of the Corpus of Global Web-Based English
Bibliographic record
Abstract
Abstract This paper presents an analysis of the pragmatic marker eh , which is typical of spoken discourse, in written online discourse from nine varieties of English using the Corpus of Global Web-based English. The analysis focuses on sentence-final eh and considers variation in terms of variety, punctuation, text type, and function. This paper also includes a variationist analysis of eh in contrast to huh . Although there are cross-variety differences, eh is used across all nine varieties in similar ways. Eh is mostly combined with a question mark, it is more frequent in blogs than in general websites, and emphatic functions dominate over narrative and interrogative uses. A qualitative analysis of the indexicalities demonstrates that eh mainly signals orality and informality in online writing but also has specific local meanings. The variationist analysis shows that eh is preferred over huh in the Canadian and New Zealand components. This preference is even more pronounced for the British and Philippine components. In contrast, huh dominates in the US component. These results show that eh is well integrated into online writing and can be characterized as a translocal pragmatic marker as it is used globally but has developed local characteristics.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".