Dialect Mixture versus Monogenesis in Colonial Varieties: The Inevitability of Canadian English?
Bibliographic record
Abstract
Abstract Similarities between varieties of the same languages can be explained in terms of shared retentions or innovations. Conversely, differences can be explained in terms of new vocabulary items, divergent changes, language contact, and dialect contact. The latter has been challenged by proponents of monogenetic theories. Evidence for and against monogenetic hypotheses are considered on the basis of two case studies. First, I demonstrate that the dialect enclave of Lunenberg County, Nova Scotia, is a mixed colonial dialect. Second, I argue that the phenomenon of Canadian Raising is the result of dialect mixture. The Canadian English data provide evidence for a connection between dialect contact, mixture, and genesis. The data support the idea that there is a deterministic outcome in situations where the target language is not spoken by a prior-existing population, which in turn accounts for why widely separated varieties of English are similar.
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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 | 0.006 | 0.112 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".