R v Campbell: Rethinking the Admissibility of Rap Lyrics in Criminal Cases
Bibliographic record
Abstract
R v Campbell is one of the few cases in North America to exclude rap lyrics as evidence of guilt in criminal cases. Unlike in Canada, the issue of criminalizing rap has received considerable attention in the United States. This article begins by documenting the Canadian experience. It is a response to the call for research by two leading American scholars on the phenomenon of putting rap on trial, Professors Charis Kubrin and Erik Nielson. After documenting and discussing 36 Canadian cases, the article examines the Supreme Court of Canada decision in R v Simard and the two leading trial decisions R v Campbell and R v Williams. Generally speaking, the Canadian cases have failed to apply a culturally competent lens when assessing probative value and, to address the relevance of race and bias, when assessing prejudicial effect. The article urges our courts to put the rap back in rap by taking a culturally competent and critical race approach to admissibility.
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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 | 0.057 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.031 | 0.057 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".