Where Have All the Articles Gone? The Use of Zero Articles in Marmora and Lake, Ontario
Bibliographic record
Abstract
In May 2019, the authors conducted sociolinguistic interviews with 39 White residents of Marmora and Lake, Ontario, a place founded predominately by British settlers, with a mixed farming/mining economy up until the late twentieth century. The data are rich in well-known dialect features, including preterit come, zero relative pronouns, and, most strikingly, zero articles. Praat analyses confirm that these zero articles are not simply due to phonetic reduction, and analysis of the zero variants exposes several trends. Among the oldest members of the population, the incidence of zero articles is relatively frequent, especially among men, a typical pattern for dialect obsolescence. In addition to phonetic conditioning, consistent with definite article reduction at earlier times, the data also show a strong effect of information status, consistent with patterns for the zero definite article in York, England. Older individuals use zero articles across more contexts compared to younger ones, suggesting systemic adjustments in an evolving grammatical system. The authors argue that the use of the zero articles in Marmora reflects an earlier stage in the history of both the definite and indefinite articles in English. They also consider cultural changes and psychological impacts from personal commentaries to highlight the importance of social context. This research demonstrates that rural Ontario offers key insight into the earlier stages and current state of dialects in North America.
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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 | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".