Same, same, but erm sort of different? Comparing three kinds of fluencemes across Australian, British, Canadian, and New Zealand English
Bibliographic record
Abstract
Although L1-English fluency has been extensively studied from many angles, few contrastive studies examine whether fluency develops similarly or differently across L1-varieties while taking sociolinguistic variation into consideration. This paper aims to close this research gap and examines the use of three core strategies of fluency (or fluencemes), i.e. discourse markers, filled pauses and unfilled pauses, across Australian, British, Canadian, and New Zealand English. These fluencemes were extracted and manually disambiguated from the private conversation sections of the respective components of the International Corpus of English (ICE-AUS, ICE-GB, ICE-CAN, and ICE-NZ). The data were normalised per speaker and linked with the sociobiographic metadata of the speakers. Analysis using random forests revealed a consistent fluenceme distribution across the four varieties, with unfilled pauses being the most common, followed by discourse markers, and then filled pauses. This pattern suggests a ‘common fluenceme core’ among L1-English varieties. The influence of sociolinguistic variables —gender, age, education, and occupation— was modest across varieties and exhibited diverse trends. Male speakers tend to use filled pauses more frequently but fewer unfilled pauses compared to female speakers. Increasing age did not significantly affect the frequency of these strategies; however, older speakers tend to use discourse markers less frequently. Both education and occupation showed a slight positive correlation with overall fluency.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".