‘Stretching’ Social Reproduction: (Re)Conceptualizing Informal and Gendered Labor in Egypt under Global Capitalism
Bibliographic record
Abstract
In an ever-growing digital economy, gig work increasingly blurs the line between formal and informal labor. Global development policies frequently champion market-driven approaches to economic progress and gender equity. However, these neoliberal discourses often disregard the gendered dynamics of global capitalism, which inherently exploit and undervalue women’s productive and social reproductive labor. Inspired by critical feminist scholarship, this paper repoliticizes social reproduction by integrating everyday life into macrolevel economic and political analysis. It proposes ‘stretching’ social reproduction as a framework for critically analyzing global capitalism, centering the lived experiences, knowledges, and relationalities of women in the Global South – groups that are often marginalized and invisibilized in neoliberal development discourses. Using data from fieldwork with 25 women gig workers in Egypt, the article examines how women learn and transfer skills, overcome material barriers and access resources, and navigate family and societal gender norms within and beyond the household. By doing so, they were disrupting gender and class hierarchies embedded in capitalism, which serve to undermine traditional knowledges, exploit communities, and separate work from everyday life. Moreover, they were (re)defining their agency as knowledge producers, active participants in economic development, and cultivators of collective community well-being in an emerging digital economy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".