“They used to follow Ø river”: The Zero Article in York English
Bibliographic record
Abstract
Speakers of York English (UK) use a zero article with definite singular nouns (e.g., “They used to follow Ø river”), which is impossible in Standard English. We probe the possibility that this form is a remnant from Old English, when there were no articles as they are currently found in Modern English, rather than a more contemporary development. We trace the diachronic trajectory of the zero article in historical-descriptive grammars and test social and linguistic constraints on its use in York English in a logistic regression analysis. The results show that information structure is a significant predictor of the zero article across all generations of the community and that the zero article is used in the same way as it was used as far back as Old English. However, it exhibits heightened usage among the older and younger generations, exhibiting a U-shaped curve. We suggest that this pattern demonstrates longitudinal maintenance of a conservative feature, which is suppressed in middle-age as the result of social pressures. In this way, this case study adds insight into the fate of dialect features in contemporary speech communities. It also highlights the importance of combining insights from different strands in linguistics for understanding the evolution of syntactic variants like the zero article.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".