The Gig Economy and Its Impact on Women in Iraq
Bibliographic record
Abstract
The gig economy has significantly transformed Iraq’s labour market, creating new opportunities for women while also exposing persistent inequalities. This paper traces the experiences of Iraqi women in the gig economy, drawing on both individual and collective insights grounded in the authors’ work in this context. These experiences reveal the dual nature of the gig economy: providing flexible work options while perpetuating vulnerabilities such as discrimination and economic insecurity. By situating our analysis within Iraq’s unique socio-economic conditions, including women’s low workforce engagement and infrastructural challenges, we contribute to a deeper understanding of the dynamics shaping women’s participation in this emerging labour market. The paper explores the types of gig work available to Iraqi women, alongside the structural barriers they face, such as limited digital infrastructure and inadequate legal protections. We conclude by highlighting actionable pathways for improving economic outcomes for women and fostering inclusive growth in the gig economy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".