The sociolinguistics of urban multilingualism
Bibliographic record
Abstract
Abstract Changing patterns of global migration and increasing ethnolinguistic (super)diversity hold sociolinguistic consequences for heritage/community languages (HCL) and majority languages in large urban centres. Studies in different cities have noted the existence of (multi-)ethnolects, which may arise from second language acquisition and/or long-term bilingualism and may take on indexical social value. This chapter compares two majority English-speaking cities in Canada (Toronto) and Australia (Melbourne) that are characterised by increasing ethnolinguistic diversity. Previous research has identified (multi-)ethnolectal behaviour in both cities that has only recently been the subject of systematic investigation. Toronto English shows different overall rates of usage of a range of phonetic/phonological and grammatical/discourse-pragmatic variables, although parallel conditioning of the variation by language-internal factors across younger speakers suggests that speakers share the same underlying system. Previous work on Melbourne English has similarly identified a number of linguistic features characteristic of particular ethnolinguistic background. Adopting the variationist sociolinguistic approach, these projects explore the function of language in constructing and expressing (ethnic) identity in situations of ethnolinguistic (super)diversity and the potential for multiple linguistic systems to co-exist.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".