Racism in the Platformized Cultural Industries: Precarity, Visibility, and Harassment in Canada
Bibliographic record
Abstract
Platform-dependent creative labor has been discussed widely in terms of the economic precarity inherent to the industry and the arbitrary ways in which algorithms structure and reinforce that precarity. I expand on these debates to articulate the role of systemic racism in structuring differential outcomes for racialized content creators by analyzing data from open-ended survey answers (N = 64), and semistructured interviews (N = 12) with racialized content creators in Canada to explore their perceptions of and experiences with platform-mediated racism. Their accounts indicate a shared understanding of how racism operates within the platformized cultural industries—be it through negative material outcomes, adverse experiences with platform algorithms, and/or through experiences of harassment. Drawing on theories of racial capitalism, institutional racism, and algorithmic bias, I provide an analysis that underscores how racism presents in multilateral, dynamic, and simultaneous ways, which compounds negative material and epistemic outcomes for racialized creators in Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".