Transatlantic perspectives on variation in negative expressions
Bibliographic record
Abstract
Negation with indefinite items in English can be expressed in three ways: any -negation ( I didn’t have any money ), no -negation ( I had no money ) and negative concord ( I didn’t have no money ). These variants have persisted over time, with some studies suggesting that the newest variant, any -negation, is increasing at the expense of no -negation (Tottie 1991a, 1991b). Others suggest that although this variable was undergoing change in earlier centuries, it is stable in Modern English (Wallage 2017). This article examines the current state of the variability in four communities within two distinctive English-speaking regions: Toronto and Belleville in Ontario, Canada, and Tyneside and York in Northern England. Our comparative quantitative analysis of speech corpora from these communities shows that the rates of no -negation vary between Northern England and Ontario, but the variation is largely stable and primarily conditioned by verb type in a robust effect that holds cross-dialectally: functional verbs retain no -negation, while lexical verbs favour any . The social embedding of the variability varies between the communities, but they share a common variable 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".