Platform Labour, Migration, And Resistance: Organizing Against Hyper-Exploitation In Paris And Toronto's Food Delivery Industries
Bibliographic record
Abstract
This doctoral thesis combines empirical research and theoretical innovations aimed at comprehending the dynamics of platform labour within advanced-capitalist economies. Through case studies in Paris, France, and Toronto, Canada, the thesis contributes to the evolving landscape of platform labour studies, migration studies, and labour geography. The over-representation of racialized immigrants engaged in platform food delivery has attracted significant attention from both academia and mainstream media, notably in Toronto with international students from India and in Paris with sans-papiers from Africa. Focusing specifically on migration and working conditions, this study unveils hyper-precarity in Euro-American cities. The primary objective of the thesis is to provide a new perspective that includes immigration and citizenship within current discourse on platform labour. Drawing inspiration from critical urban studies, migration studies, and science and technology studies, the research introduces two conceptual propositions: i) “citizen-rentier-ship”, designed to elucidate how various stakeholders benefit from precarious citizenship status, and ii) a “relational comparison” of platform labour resistance, offering insights into the evolution of the unrest against platform labour exploitation—a crucial facet of urban development. The thesis is based on extensive interviews with food riders, workers, spokespersons, and other key actors, shedding light on their capacity for self-organization within advanced capitalist societies. By exploring strategies, limitations, and the dimensions of resistance—both digital and physical—through interactions with riders and individuals who resisted deactivation, low wages, and algorithmic management, the research contributes to a nuanced understanding of the challenges and opportunities faced by these workers. The case studies place emphasis on migrant workers’ perspectives. They reshape ongoing debates about global platforms by centering attention on the bottom ends of labour markets. In conclusion, the study contends that the struggles of migrant workers are deeply entwined with labour laws, immigration policies, misclassification practices, and urban policies in France and Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".