A comparative analysis of invariant tags in three varieties of English
Bibliographic record
Abstract
Discourse markers are a feature of everyday conversation — they signal attitudes and beliefs to their interlocutors beyond the base utterance. One particular type of discourse marker is the invariant tag (InT), for example New Zealand and Canadian English eh . Previous studies of InTs have clearly described InT uses in one language variety (e.g. Berland 1997, on London teenage talk; Stubbe and Holmes 1995, on NZ English; on sociolinguistic features e.g. Stubbe and Holmes 1995 and on single markers e.g. Avis 1972; Love 1973; Gibson 1977; Meyerhoff 1992 and 1994; Gold 2005, 2008 on eh ). However, the class of InTs has not yet been fully described, and the variety of approaches taken (corpus- and survey-based) does not easily allow for cross-varietal or cross-linguistic comparison. This study investigates InTs in three varieties of English from a corpus-based approach. It lists the InTs available in New Zealand, British and Indian English through their occurrences in their respective International Corpus of English (ICE) corpora, and compares usages of four tags across the varieties. The description offers a clearer overview of the InT class for descriptive grammars, as well as more explicit definitions and usage guides for e.g. EFL/ESL pedagogy. An unambiguous description of several InTs and their meanings will also allow more thorough comparison in studies of other English varieties. Finally, the results offer another viewpoint on the issue of representativeness in corpora with respect to regional versus national varieties of the Englishes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".