Linguistic variation and ethnicity in a super-diverse community: The case of Vancouver English
Bibliographic record
Abstract
Today, people with British/European heritage comprise about half (49.3%) of the total population of Metro Vancouver, while the other half is represented by visual minorities, with Chinese (20.6%) and South Asians (11.9%) being the largest ones (Statistics Canada 2017). However, non-White population are largely unrepresented in sociolinguistic research on the variety of English spoken locally. The objective of this study is to determine whether and to what extent young people with non-White ethnic backgrounds participate in some of the on-going sound changes in Vancouver English. Data from 45 participants with British/Mixed European, Chinese and South Asian heritage, native speakers of English, were analyzed instrumentally to get the formant measurements of the vowels of each speaker. Interview data were subjected to thematic analysis that aimed to describe to which extent each participant affiliated with their heritage. The results of the descriptive and inferential statistical analysis showed that, first, the vowel systems of these young people are similar and they all are undoubtedly speakers of modern Canadian English as described in previous research (Boberg 2010). Second, all three groups participate in the most important changes in Canadian English: the Canadian Shift, Canadian Raising, the fronting of back vowels, and allophonic variation of /æ/ in pre-nasal and pre-velar positions. Some differences along the ethnic lines that were discovered concern the degree of advancement of a given change, not its presence or absence. Socio-ethnic profiles of the participants created on the basis of the thematic analysis can be roughly put into two categories, mono- and bicultural identity orientation (Comănaru et al. 2018). Great variability is described both within and across groups, with language emerging as one of the most important factors in the participants’ identity construction. Exploratory analysis showed some tendencies in vowel production by speakers with mono- and bicultural orientations, with differences both among and within two non-White groups. The findings of the study call into question both our understanding of the mechanisms of language acquisition and our approach to delimiting and describing speech communities in super-diverse urban centers.
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.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".