Like as a discourse marker in different varieties of English : A contrastive corpus-based study
Bibliographic record
Abstract
The central objective of this master’s thesis is to explore the use of the discourse marker like across five varieties of Inner Circle English, namely American, British, Canadian, Irish and New Zealand English. The innovative character of this study lies in its contrastive dimension. An analysis of the use of like was carried out on data from the ‘direct conversations’ section of the SBC, ICE-GB, ICE-CA, ICE-IR and ICE-NZ in order to identify the similarities and differences between these subcorpora in terms of frequency of use, position, function and sociolinguistic determinants of the discourse marker like. The quantitative analysis reveals important differences across the five varieties under study: the discourse marker like is significantly more frequent in Irish English than in the other subcorpora and British English comes in final position in terms of frequency. From a qualitative point of view, the results of the analyses show a strong tendency shared by the five subcorpora for the discourse marker like to occur in utterance-medial position with a focusing function. Finally, the analysis of speaker’s gender and age reveals that the discourse marker like is most prominent among 19 to 24-year old females in the SBC and among 31 to 40-year old males in ICE-CA. The influence of those two sociolinguistic variables appears to be limited, however, since there is a great deal of variation across individual speakers’ use of like.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".