Driving Gigs in Oman: Women and Techno-Fixes in the Platform Economy
Bibliographic record
Abstract
Digital platforms mediating work between customers and service providers have expanded exponentially in the past decade, driving a growing research agenda on the impact of platform capitalism, AI and the gig economy on labour around the world. This paper is interested in understanding the platform economy at the intersection of gender with the political economy of labour. Focusing on the Omani case of a new women’s taxi service (OFemale) through the digital platform OTaxi, it asks how ride-hailing platforms are impacting women’s employment futures. Using rapid ethnography, elite interviews and a survey, the article examines both the launch and expansion of the business alongside the experiences of Omani women as taxi drivers. The article excavates three gendered discourses of freedom, protection and job creation around platform labour and female labour market participation in the region. It argues that digital platforms such as OTaxi offer techno-fixes to fill gaps in the market and respond to the need to generate job opportunities for female citizens in the country. At the same time, women make use of these opportunities and interpret their experience in diverse ways that problematise the neo-liberal promises of innovative technologies, job flexibility and autonomy embodied in platform capitalism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| 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".