“We Don’t Have Those American Problems”: Anti-Black Practices in Canada’s Rap Music Marketplace, 1985–2020
Bibliographic record
Abstract
Beginning in the early to mid 1980s, Hip Hop culture appeared on Canadian stages and in homes, even as it was limited in supply on commercial radio and television. Unlike their American counterparts, mainstream Canadian emcees (many of whom were racialized as Black and identified with the city of Toronto) were notably dependent upon personal finances, under-resourced independent record labels, distribution deals, and state and not-for-profit grant monies to subsidize the conceptualization, production, and promotion of their art. Labelled “urban music” in an attempt to spatialize and covertly reference Blackness, Hip Hop in Canada, from the outset, was mapped against, in conflict with, and outside of the national imaginary. While building local scenes, an independent label system, and a cross-Canada college radio, television, and live music infrastructure and audience, Hip Hop artists developed spaces of resistance, circumvented industry-generated obstacles, and defined success on their own terms — all of which suggested that they were not solely at the will of the dominant white music industry. And yet artists simultaneously encountered anti-Black practices that constrained the creation and sustenance of a nationwide Hip Hop infrastructure and denoted an inequitable structuring of support for the arts in Canada. By examining the interface of Blackness, art, and the racial economy of Canada’s creative industries, this article will outline instances of Canada’s anti-Black racism as well as the challenges Hip Hop artists and industry professionals have faced in the areas of recording and label relations, music sales, broadcasting regulations, and the accolade system. These social relations — many of which are rooted in longer histories of race relations and anti-Blackness in Canada — resulted in industry-wide policies, practices, norms, and ideologies that unfairly disadvantaged Black artists and undermined the realization and marketplace potential of a Hip Hop infrastructure within and beyond Canada.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.008 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".