Defying the "Illiberal" Gig Economy: Coping Strategies of Freelance Domestic Workers in the United Arab Emirates
Bibliographic record
Abstract
How do low-skilled migrant workers navigate restrictive gig economies in illiberal host states of the Global South? Despite the growing gig economy, scholars have yet to examine the linkage between the politics of the gig economy and migrant resilience in illiberal host states in the Global South. Using a single case study of freelance Filipina domestic workers in the UAE (N = 20), I argue that, despite facing legal and economic risks (penalties), freelance migrant workers have produced an informal freelancing visa system to contest the formal and hierarchical segmentation of the gig economy via three diverse strategies: co-optation, tapping and brokering. These evasive social coping strategies mirror their collective resistance against structural labour exploitation and reinforce their autonomous role in the social (re)production of community solidarity within informal gig economies. Overall, this study contributes to empirical and theoretical discourse on the politics of illiberal migration management and the gig economy by featuring female migrant freelancers' complex social agency within illiberal gig economies in the Global South.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".