The sociolinguistics of urban multilingualism
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
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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it