The most stable it's<i>ever</i>been: the preterit/present perfect alternation in spoken Ontario English
Bibliographic record
Abstract
English tense/aspect-marking is an area where variation abounds and where many theories have been formulated. Diachronic studies of the preterit/present perfect alternation indicate that the present perfect (e.g.I have eaten already) has been losing ground to the preterit (e.g.I ate already) (e.g. Elsness 1997, but see Hundt & Smith 2009, Werner 2014). However, few studies have examined this alternation in vernacular speech. This article fills this lacuna by analyzing spoken data from Ontario, Canada, from an apparent-time perspective. Using a large archive of multiple communities and people of different generations, we focus on linguistic contexts known to be variable, viz. with adverbs of indefinite time. Results indicate that, in contrast with previous studies, the alternation is mostly stable. We find evidence of change only with the adverbever. Where there is evidence of change, this change is different from the predictions in the literature, with the preterit increasing in frequency. We suggest that a minor constructionalization process operates in tandem with ongoing specialization of the preterit/present perfect contrast. Taken together, these results provide another example of the importance of including speech in research on language variation and change and of the unique contribution certain constructions make to more general systems of grammar.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".