Gendering digital labor: work and family digital communication across 29 countries
Bibliographic record
Abstract
With rapid digitalization, people increasingly use information and communication technologies (ICTs). Analyzing European Social Survey data across 29 countries, we address an under-researched question: how is the labor of using ICTs for digital communication gendered across the domains of work and family? Using latent profile analysis, we identify five profiles of work-family digital communication – dual-medium (most prevalent), dual-low, high work-only, dual-high, and high family-only (least prevalent) – with notable gender differences. Women are less likely than men to have high work-only but are more likely to have high family-only and dual-high work-family digital communication. Multilevel models reveal that among those with better digital literacy and those who work from home more often, there are wider gender gaps whereby women are more likely than men to juggle dual-medium work-family digital communication. In countries where people use the internet more intensely, women are more likely than men to specialize in family-only and juggle dual-high work-family digital communication. As digital literacy, working from home, and internet use intensity increase further, women may disproportionately take on family-related digital communication and also suffer from a ‘digital double burden’ in work-family life. Our findings highlight new forms of gender inequality in the division of labor in the digital era.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".