The Sentence-Final 'but' in American/Canadian English and Australian/New Zealand English: A Comparative Analysis Based on the TV Corpus
Bibliographic record
Abstract
Sentence-final particles (SFPs) are a common language phenomenon. Existing studies mainly focus on SFPs in single dialects or their cross-language comparisons. This article conducts a comparative analysis of the sentence-final 'but' in American/Canadian English and Australian/New Zealand English based on the TV Corpus. It focuses on its overall frequency, semantic-pragmatic roles and values. The TV Corpus, containing 325 million words from numerous TV episodes, is used as the data source, with 30 random examples analyzed in each dialect context. Results show that the sentence-final 'but' has a higher proportion per million words in Australian/New Zealand English. Both dialects mainly use it as discourse markers but differ in semantic-pragmatic values: cataphoric value is most common in US/CA English, while contrastive value prevails in AU/NZ English. The patterns may be attributed to historical influences, dialect development, and conversational style. This study provides a new perspective for the research of intra-language dialectal contrasts in SFPs.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".