Platformized labour and harassment in Canada: Quantifying the effects of racism on content creators
Bibliographic record
Abstract
Digital content creation is a growing area of labour in Canada. Alongside the development of this labour market, it has been reported there are rising issues of harassment, racism, and racial representation. Research germane to this area has provided rich qualitative accounts of how harassment and social oppression impact marginalized content creators. This study builds on this scholarly area to demonstrate quantitatively the ways in which harassment manifests in a Canadian setting. Using data from an online survey targeting Canadian content creators ( N = 103), I specifically examine the incidence of harassment and racism among this population. Drawing on critical race theories, I argue that although online harassment is a widespread workplace hazard for content creators – regardless of identity, the consequences of this harassment are qualitatively different for those who have been historically marginalized. I expand on these findings to articulate how these impacts have downstream effects for marginalized creators, which may hinder their ability to sustain their labour in this environment. Finally, I situate these findings in the platformized environment within which these workplace hazards exist and problematize the arms-length approach that platforms take in regulating these hazards.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".