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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".