“He Yells at Me Three Times: Stay in Your Car!”: Rap Culture, Black Identity and Reception of the Message of Police Brutality in Quebec City
Bibliographic record
Abstract
An earlier version of this paper has been presented online at the annual Conference of Canadian Sociological Association, during the Congress of Humanities and Social Sciences in Toronto in June 2025. It explores how rap music in Quebec, particularly through the work of the rap artist Webster and his collective Limoilou Starz (LS), serves as a tool for meaning-making, political expression, and resistance against racial profiling and police brutality experienced by Black youth in Quebec City. The study focuses on the song “SPVQ” (Service de police de la Ville de Québec) as a case study to analyze the encoding and reception of anti-racist messages within rap culture. Webster draws from a global tradition of socio-political rap—like that of African-American rappers KRS-One, LL Cool J, and Tupac—while anchoring his critique in the local realities of Limoilou, a marginalized, racially diverse district of Quebec City. Through a narrative, figurative, semantic, and ideological analysis of the song, the paper reveals how Webster articulates themes of police brutality, structural discrimination, economic marginalization, and resistance. The rapper’s message is both a form of testimony and a civic intervention. His broader activism is analyzed through ethnographic techniques—including participant observation and interviews during workshops, media appearances, and online campaigns. It translates these messages into tangible social practices. To explain the persistence of these injustices, the paper situates the issue within Quebec’s interculturalism framework, which ostensibly promotes dialogue and integration but often masks or even reinforces structural racism. This model maintains a symbolic majority/minority duality and conditions inclusion on conformity to a dominant White Francophone identity, thus rendering racialized youth as perpetual outsiders.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".