Are Women Not ‘Working’? Interactions between Childcare and Women’s Economic Engagement
Bibliographic record
Abstract
This paper seeks to examine how childcare impacts upon women’s economic engagement in India, Nepal, Tanzania, and Rwanda. In delineating the linkages between childcare, paid work, and other tasks that women carry out within and outside the house, this paper privileges women’s own perceptions of childcare as ‘work’, and the extent to which they see this as a tension between women’s caregiving role and their income-generating role. Our findings corroborate that women experience significant trade-offs as they engage in both \nmarket activities and childcare tasks. We highlight the important distinction between direct and supervisory childcare – with supervisory childcare taking up a large amount of women’s time across all contexts. In bringing women’s voices to the fore of the prevalent discourse of childcare being a ‘barrier’ to women’s paid work, this paper highlights the complex and bidirectional relationship between childcare and women’s economic engagement. Our analysis shows that for women from lower-income households, the effect of childcare on women’s engagement in paid work (hours, location, type, or nature of work) is mediated by \ndifferent factors: (a) the economic condition of the household; (b) the availability of alternative care arrangements; (c) the household structure and; (d) alternative options (for both men and women) for paid work. This research highlights how complex and constrained women’s choices are, in a context of low-paid jobs and lack of support for childcare from other institutional actors, and how women posit childcare as a positive and desirable experience.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".