Bibliographic record
Abstract
English has many words to refer to an adult man (e.g., man, guy, dude), and these are undergoing change in the Ontario dialects. This article analyzes the distribution of these and related forms using data collected in Ontario, Canada. In total, 6,788 tokens for 17 communities were extracted and analyzed with a comparative sociolinguistics methodology for social and geographic factors. The results demonstrate a substantive language change in progress with two striking patterns. First, male speakers in Ontario were the leaders of this change in the past. However, as guy gained prominence across the twentieth century, women started using it as frequently as men. Second, these developments are complicated by the complexity of the sociolinguistic landscape. There is a clear urban versus peripheral division across Ontario communities that also involves both population size and distance from the large urban center, Toronto. Further, social network type and other local influences are also important. In sum, variation in third-person singular male referents in Ontario dialects provides new insight into the co-occurrence and evolution of sociolinguistic factors in the process of language change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".