After a Global Platform Leaves: Understanding the Heterogeneity of Gig Workers through Capital Mobility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it