Multilingual Codeswitching in Quebec Rap: Poetry, Pragmatics and Performativity
Bibliographic record
Abstract
Quebec rap lyrics stand out on the world Hip-Hop scene by virtue of the ease and rapidity with which performers in this multilingual, multiethnic youth community codeswitch, frequently among three or more languages or language varieties (usually over a French and/or English base) in the same song. We construct a framework for understanding ‘artistic code-mixing’ in Quebec Hip-Hop, which may involve languages rappers do not profess to speak fully and upon which they have no ethnic ‘claim’. Lyrics were analysed according to their functions in respect to pragmatics (rapper signature, vocative, discourse-marking), poetics (facilitating internal rhyme), and performing multiple identities. Analysis was by origin of lexical item, type of switch (lexical, morphological, syntactic, phonological), and discourse function (getting attention, rhyming). Language choices made involve both codeswitching and the choice of languages themselves. Switching strategies perform functions of both ‘globalisation’ and ‘localisation’, and is exploited by individuals in different ways, but are fundamentally linked by a positioning of multilingualism as a natural and desirable condition. This study is the first to explore Hip-Hop codeswitching in the linguistic-sociopolitical context of post-Bill-101 Quebec. It illuminates a new way in which Québécois youth are challenging official definitions of ethnic and speech communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".