Bibliographic record
Abstract
What is the impact of the so-called gig economy on women workers in the Middle East? Does digitalisation represent a catalyst for female labour participation in the region or a burden leading to further financial insecurity and invisibility? How are ordinary women gig workers re-imagining their tech lives and challenging unwritten rules, patriarchy and lack of access to the labour market? Featuring articles analysing case studies in Egypt, Iraq, Oman and the United Arab Emirates, this special issue addresses the abovementioned questions, directly speaking to the academic debate on the global gig economies. Proving a regional and local perspective, it contributes to a more plural understanding of gig work in a multiplicity of contexts, practices and experiences. It investigates the relationship between the daily and the digital to explore the role of platforms in shaping female labour participation and women’s empowerment, as well as issues of precarisation and marginalisation. By proposing a collection of original and pioneering research on an understudied topic as applied to specific contexts in the Middle East, the special issue broadens the analysis of the so-called gig economy beyond a mere economic lens, bringing together multi-disciplinary insights and approaches from sociology, political economy and digital anthropology. It shows that online gig work is neither a crystallised nor monolithic dimension. Instead, platforms - in some instances - have become vectors of formalisation instead of leading only to informality, such as in the case of taxi driving app and home cooking/food delivery, where apps have enhanced more regulation as formality was not the norm before. Women gig workers are re-imagining their roles in their everyday practices of working from home, blurring the lines between the public and the private spheres. They adapt to neoliberal conditions of flexibilisation to sustain their needs in contexts where processes of labour informalisation have long permeated the development of labour relations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".