How'd you get that accent?: Acquiring a second dialect of the same language
Bibliographic record
Abstract
This article presents a case study of second dialect acquisition by three children over six years as they shift from Canadian to British English. Informed by Chambers's principles of second dialect acquisition, the analysis focuses on a frequent and socially embedded linguistic feature, T-voicing (e.g., pudding versus putting). An extensive corpus and quantitative methods permit tracking the shift to British English as it is happening. Although all of the children eventually sound local, the acquisition process is complex. Frequency of British variants rises incrementally, lagging behind the acquisition of variable constraints, which are in turn ordered by type. Internal patterns are acquired early, while social correlates lag behind. Acceleration of second dialect variants occurs at well-defined sociocultural milestones, particularly entering the school system. Successful second dialect acquisition is a direct consequence of sustained access to and integration with the local speech community.We would like to thank Tara, Shaman, and Freya for their patience and humor in letting us analyze these materials, and especially for the hilarity of their antics, which added greatly to the amount of fun we had in figuring out their second dialect acquisition. This study was inspired by and has also profited from many discussions with our mentor and friend Jack Chambers. We have also benefited from the insightful guidance of Peter Trudgill, both in print and in personal commentary. An anonymous reviewer added an additional perspective. Of course, none of them is responsible for any remaining shortcomings of our analysis or interpretation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".