Bibliographic record
Abstract
This study considers the spread of discourse like in Quebec English. Although several previous studies have examined the pragmatic functions and rate of use of like as a discourse marker, few consider its interaction with the syntactic structure and most focus solely on English-dominant communities. Thus, while D’Arcy (2005) has shown that this discourse feature is spreading systematically throughout the syntactic structure in apparent time in Toronto, it is unknown whether its evolution is as advanced in communities such as Quebec, where English is a minority language, isolated from mainstream varieties. We analyze the rate of use of discourse like in three distinct structural contexts (CP, DP, and vP) by 39 native English speakers from the Quebec English Corpus (Poplack, et al., 2006). Speakers from both Montreal and Quebec City were included in this study since the degree of isolation from mainstream English is arguably greater in the latter. Internal grammatical factors and external factors are also analyzed. The results show that while both Quebec City and Montreal speakers exhibit substantially lower rates than Toronto speakers in their use of like in each of the structural contexts examined, the internal conditioning of like in Quebec English is practically identical to that in Toronto English. These findings only partially support the hypothesis that these speakers’ isolation from mainstream English causes them to lag behind in ongoing change and highlight the complexity involved in the exploration of such a widespread and multifaceted phenomenon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.534 | 0.290 |
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".