Quantifying the referential function of general extenders in North American English
Bibliographic record
Abstract
Abstract Discourse markers (like, I don't know, etc.) are known to vary in frequency across English dialects and speech settings. It is difficult to make meaningful generalizations over these differences, since quantitative discourse-pragmatic variation studies ‘lack [a] coherent set of methodological principles’ (Pichler 2010:582). This has often constrained quantitative studies to focus on the form, rather than the function of discourse-pragmatic features. The current article employs a novel method for rigorously identifying and quantifying the referential function (set-extension) of general extenders (GEs), for example,and stuff like that, or whatever. We apply this method to GEs extracted from three corpora of contemporary North American English speech. The results demonstrate that, across varieties, (i) referential GEs occur at a comparable proportional rate in vernacular speech, and (ii) referential GEs are longer than nonreferential GEs. Collectively, these findings represent a step towards comparative quantitative studies of GEs' functions in discourse. (Discourse-pragmatic variation, general extenders, methodological approaches, American English, Canadian English)
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".