After a Global Platform Leaves: Understanding the Heterogeneity of Gig Workers through Capital Mobility
Bibliographic record
Abstract
We know a great deal about global capital mobility in traditional industries, such as manufacturing, but very little about emerging capital mobility in the gig economy. Using the case of Canadian Foodora, a multinational platform that left Canada in 2020, I situate global capital mobility in the local labour market. Drawing upon interview data with former Foodora couriers and ethnographic data collected from a gig workers’ union, I investigate the social, economic and political subjectivities of gig workers activated by a global platform’s capital mobility. My findings reveal unexpected parallel effects caused by capital mobility in the gig economy and traditional industries. My research highlights how heterogeneity is salient for understanding divergent worker subjectivities. The economic and social impacts upon financially dependent gig workers and the emotional connections of devoted and organized gig workers challenge the dominant discourse that gig workers are simply part-timers and hence free from work commitments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".