Dialect Identification Across a Nation-State Border: Perception of Dialectal Variants in Seattle, WA and Vancouver, BC
Bibliographic record
Abstract
The Atlas of North America English distinguishes "the West" from "Western Canada" on the basis of /æ/ retraction and Canadian Raising (Labov, Ash, and Boberg 2006). Since the Atlas, scholars have provided a more detailed understanding of /æɡ/ raising, /æ/retraction, and Canadian Raising throughout the Western United States and Western Canada (Boberg 2008, Fridland et al. 2016, Presnyakova, Umbal, and Pappas 2017, Roeder, Onosson, and D'Arcy 2018). In a production study, Swan (2016) found that Seattle and Vancouver, BC are differentiated primarily by Canadian Raising and pre-nasal raising of /æ/ and show minimal difference with respect to /æɡ/ raising and /æ/ retraction. Seattle and Vancouver speakers also shared different ideologies about their speech: Seattle respondents felt more confident that they could identify a Vancouver talker based on speech than vice versa. The current study builds from these observations to ask how natives of Seattle and Vancouver perceive the similarities and distinctions documented in the production literature. Can listeners differentiate a talker as being from Seattle or Vancouver? What cues are listeners relying on to judge a talker as being from Seattle or Vancouver? Do these perceptual cues align with the production differences between the cities? What does this imply for a dialectology of the West? These questions are addressed using a forced-choice dialect identification task using the variables represented by FAN, PATH, TAG, and DEVOUT. Our analysis considers signal detection theoretic measures to elucidate sensitivity and bias (Macmillan and Creelman 2005). The results suggest that differentiating Seattle and Vancouver talkers is a challenging task for listeners native to these cities. Neither Seattle nor Vancouver listeners show very accurate performance for any of the single-word stimuli or short phrase blocks of the task and are generally not able classify a talker's city of origin based on their speech. The most accurate performance emerges for Seattle listeners classifying talkers saying DEVOUT, which aligns with the production differences between the cities and is likely driven by stereotypes about Canadian English. Listeners from both cities show more own city bias for the phonetic features that are shown to be more similar across the cities (PATH and TAG) than for those shown to be more different in production (FAN and DEVOUT). A closer look at bias reveals that while Seattle listeners perform with slightly more accuracy, they also show more own-city bias. We discussion possible reasons for this pattern and implications for dialectology of the West and Western Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".