How do Torontonians hear ethnic identity?
Bibliographic record
Abstract
More attention is being paid to the sociolinguistic consequences of urban ethnolinguistic diversity, but the origins and social meanings of ethnolects are not well understood and their role in marking ethnic identity untested. Anecdotal remarks and media attention point to Canadians’ awareness of ethnically marked ways of speaking English but despite public interest, sparse research exists on perceptions of different ways of speaking. We report the results of a pilot project addressing perceptions of ethnically-marked ways of speaking English in Toronto, Canada’s largest and most ethnically diverse city. To test Torontonians’ ability to identify native speakers of Toronto English from different ethnic groups, we ask ~100 participants to listen to speech excerpts produced by 18 Torontonians from five of the largest ethnic groups in the city (British/Irish, Chinese, Italian, Portuguese and Punjabi). Participants were asked to identify the speakers’ ethnic backgrounds, indicate how well they think the person speaks English, and whether they believe them to be from Toronto. Results confirm that Torontonians are aware of ethnically marked ways of speaking and are better able to identify speakers who affiliate more strongly with their ethnicities. Judgments of speaking English well are tied more closely to perceived than actual ethnicity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".