Towards a political economy of immigrant languages and multilingualism
Bibliographic record
Abstract
The growing number of immigrants in countries like Australia, Canada, the United Kingdom, and the United States has drawn increasing attention from applied linguists, language policy researchers, and migration scholars. Their work highlights how language mediates the the recognition of foreign education and skills, the allocation of resources, economic integration, while reinforcing ideologies associated with dominant languages like English. Yet, much of the research on the economics of language remains largely restricted to monolingual or bilingual contexts centered on mainstream economies. In immigrant-receiving countries like Canada, changing social dynamics have produced new forms of economic organization through ethnic economies, ethnic concentrations, and social multilingualism that are shaped by migration, mobility, and the digital connectivity. Understanding these alternative or parallel to mainstream economies requires an economics of multilingualism approach that accounts for the dynamic role of language in the shaping economic participation and opportunity. Through a case study of South Asian languages in Alberta, this paper examines how immigrant languages are used for economic purposes, making economic activities dynamic, complex, and multilingual, and how such research can stretch the political economy focus from monolingual and bilingual to multilingual economies.
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 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 | 0.003 | 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 | 0.006 | 0.027 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".