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Record W4407196369 · doi:10.15173/glj.v16i1.5880

The “Daily Digital”: (Re)imagining Technology in Home-Based Women’s Gig Work in Egypt.

2025· article· en· W4407196369 on OpenAlexaffvenue
Laila Mourad

Bibliographic record

VenueGlobal Labour Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsYork University
Fundersnot available
KeywordsWork (physics)SociologyHistoryArtEngineering

Abstract

fetched live from OpenAlex

The gig economy is (re)shaping work and revolutionising the use of technology in everyday life. In Egypt, where more than 50 per cent of women’s enterprises are home-based, digital tools such as smartphones and social media are integral to managing informal labour practices. This paper challenges neoliberal development narratives by introducing the Daily Digital framework, a decolonial feminist lens that centres the relational and experiential dimensions of technology use. Unlike existing frameworks, it repositions the household as a site of innovation and economic agency, emphasising women’s creative strategies for (re)imagining technology and integrating it into their daily lives and work. Based on fieldwork conducted in Egypt in 2022 and 2023 with 25 home-based online food vendors, I demonstrate how women gig workers use their socially reproductive knowledges and relationalities to transform technology into a versatile tool for navigating and overcoming structural, material and social barriers, while (re)claiming and redefining their agency and mobility. This research contributes to feminist and decolonial scholarship by centring the lived experiences of women in informal economies and providing a new lens to theorise the intersections of technology, gender and labour. The Daily Digital framework offers valuable insights for (re)imagining gig work and advancing research and policy in the Global South.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.250
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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