Responses to Thanks in Ireland, England and Canada: A Variational Pragmatic Perspective
Bibliographic record
Abstract
Abstract The present study investigates responses to thanks across the varieties of Irish English, English English and Canadian English. Data are taken from the Lueneburg Direction-Giving (LuDiG) corpus, a specialised corpus of spoken direction-giving exchanges across pluricentric varieties constructed using Labovian-style methods. The analysis centres on the cross-varietal pragmatic choices made in responding to thanks on the level of tokens, types and strategies. Findings point to the broad universality of realisations of responses to thanks across the pluricentric varieties at hand. Variety-preferential choices are, however, also recorded on a national level, particularly in type and strategy preferences. While all varieties use a ‘minimising the favour’ strategy extensively, this strategy is employed to a comparatively higher degree in the Irish English and English English data. In contrast, the speakers of Canadian English use an ‘expressing appreciation of the addressee’ strategy to a comparatively larger extent. Speakers of Canadian English are suggested to orient more strongly to positive face needs, and speakers of Irish English and English English more strongly towards negative face needs. The paper also discusses the methodological challenges of contrasting spoken interactional data for cross-varietal pragmatic speech act analyses and shows some strengths of specialised corpora in this regard.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".