Heroes or zeros? Portuguese–Canadian youth and the cost of mobilising different sociolinguistic resources
Bibliographic record
Abstract
This critical sociolinguistic paper adopts a materialist view of how multiple languages and identities are negotiated in an effort to re-examine multilingualism and why people invest in certain sociolinguistic practices. The focus is on the social and linguistic resources and performances of Portuguese–Canadian youth in student cultural associations or clubs in Toronto. The sociolinguistic work carried out in these Portuguese clubs provides a rich context in which to explore diverse linguistic repertoires and trans-local identities which are the hallmark of a postmodern, globalised world. These student associations are flexible transnational spaces where young people should be free to construct their multiple sociolinguistic identities as they see fit. They should be able to assert their creative agency without the constraints of traditional ethnolinguistic gate-keepers like Portuguese teachers, parents or community leaders who can impose homogenised or ‘legitimate’ ways of speaking and being Portuguese. Yet, this hypothetical freedom and flexibility are not easily achieved and they often come at a cost. At issue are questions surrounding authenticity, legitimacy, dominant linguistic and nationalist ideologies, and access to material and symbolic capital that structure the Portuguese–Canadian community as a market and position some people as ‘heroes’ of the Portuguese cause, and others more like ‘zeros’.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".