Canadians Redefining R&B: The Online Marketing of Drake, Justin Bieber, and Jessie Reyez
Bibliographic record
Abstract
In a country that long failed to accept, include, and institutionalize R&B music as part of Canadian culture, musical artists Justin Bieber, Drake, and Jessie Reyez have successfully broken-down barriers by having successful careers as racially diverse Canadian R&B artists. This qualitative study surveys the literature on classifications of the R&B genre and of Canadian identities in popular media. The theoretical framework of discourse analysis is used to conduct a brief episodic history of Canadian R&B and to evaluate how the music genre “R&B,” is traditionally associated with people who have "Black" and "American" identities, and how a “Canadian” identity is traditionally associated with “white” and “folk” musical artists. I conclude that the ascription of racialized and nationalized identities is found to play a role in each artist's respective inclusion, exclusion, and/or authentication vis a vis R&B. I evaluate how Bieber, Drake, and Reyez each articulate “R&B-ness” and “Canadian-ness” to represent multiple, yet equally Canadian national narratives through their Canadian R&B artist lifestyle brands. In exploring ideas of national identity, intersectionality, digital celebrity, branding, and marketing related to contemporary Canadian popular music genres, the dissertation seeks to answer the question: How have the careers of Justin Bieber, Drake, and Jessie Reyez reinforced, complicated, and/or challenged hegemonic understandings of both “Canadian-ness” and “R&B-ness”? Through textual analyses of their social media posts, brand partnerships, interviews, music videos, and music lyrics, the dissertation traces out how multicultural Canadian artists Bieber, Drake, and Reyez broke into the music industry as “digital stars” (Harvey, 2017) by using online communication strategies, alongside traditional industry practices (such as networking with music industry gatekeepers). A particular focus involves Drake’s, Bieber’s, and Reyez’s brand partnerships and social media strategies, between 2019 and 2022, when the COVID-19 pandemic accelerated the significance of online communications, and the Black Lives Matter movement encouraged changes to race-based music industry classifications. The dissertation includes insights from interviews conducted with 35 U.S. and Canadian marketing professionals and music industry executives in 2020. This study is applicable to explorations of how race, nationality, and music genre categories are classified, cultural branding, and contemporary marketing strategies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".