Gig work as migrant work: The platformization of migration infrastructure
Bibliographic record
Abstract
With markets concentrating predominantly in and around large cities, gig platforms across the globe seem to depend as much on the cheap labor of migrants and minorities as on investment capital and permissive governments. Accordingly, we argue that there is an urgent need to center migrant experiences and the role of migrant labor in gig economy research, in order to generate a better understanding of how gig work offers certain opportunities and challenges to migrants with a variety of backgrounds and skill levels. To fill this research gap, this article examines why migrant workers in Berlin, Amsterdam, and New York take up platform labor and how they incorporate it into their everyday lives and migration trajectories. Additionally, it considers the extent to which gig platforms are emerging as actors in the political economy of migration, as a result of how they absorb migrant labor and mediate migrant mobilities. We move beyond the existing parameters of gig economy research by engaging with two strands of literature on migration and migrant labor that, we feel, are particularly useful for framing our analysis: the autonomy of migration approach and the migration infrastructures perspective. Combining these conceptual lenses enables us not only to critically situate migrant gig workers’ experiences but also to identify a broader development: the platformization of low-wage labor markets that are an integral component of migration infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".