Bibliographic record
Abstract
Abstract This study adopts Nelson’s (2014) methodological framework to investigate core and peripheral lexical bundles (i.e. recurrent multi-word sequences) in conversation, using data from the British, Canadian, Singapore, and Hong Kong components of the International Corpus of English (ICE). The overlap and non-overlap comparisons reveal (dis)similarities in the use of bundles across the four World Englishes (WEs). Our findings suggest that in terms of discourse building blocks, the more advanced a variety is according to Schneider’s (2007) Dynamic Model of New Englishes, the more lexical bundles it shares with the common core in conversation. Canadian English (CanE) shares the most common ground with British English (BrE). As a nascent variety, Hong Kong English (HKE) differs most from BrE, while Singapore English falls between CanE and HKE. Though the results do not correlate with Schneider’s Dynamic Model at the level of recurring chunks, they allow us to test predictions of WEs models. Quantitative and qualitative analyses enable the identification of bundles with significantly high frequency in each regional variety, thus enriching comparative research of WEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".