How'd you get that accent?: Acquiring a second dialect of the same language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it