Class identity vs intersectional solidarities: Divergent models for organizing gig workers in Seoul and Toronto
Bibliographic record
Abstract
Studies indicate that gig workers, one of the leading groups revitalizing labor movements globally, have organized by diverging from traditional union strategies. How do they achieve this in diverse local contexts? Drawing on 21 months of international ethnographic fieldwork with gig workers’ unions in Seoul and Toronto, this article examines how and why these two unions develop different strategies for addressing critical crises. Comparative analysis reveals that while the shared labor process and the multinational parent company drive the unions toward new unionism, different worker subjectivities are emphasized by each union based on specific axes of oppression: working-class citizen men in Seoul and racialized immigrants in Toronto. These union orientations are linked to the unions’ distinct histories, including the biographies of founding members. My argument is twofold. First, to better understand rising gig workers’ organizing efforts around the globe, we must consider both global and local contexts. While gig labor processes push gig workers’ unions to move away from traditional union tactics, two key local factors—the workforce’s demographic makeup and union histories—shape their divergent models. Second, it is critical to understand the process of cultivating solidarity—not only building solidarity itself but also deciding which groups to be in solidarity within the local context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".