Patterns of Paid and Unpaid Work: The Influence of Power, Social Context, and Family Background
Bibliographic record
Abstract
Over the last several decades there have been changes in how paid and unpaid labour is divided between men and women: The rate of women's participation in the labour force women has increased as has men's participation in household labour. Although a plethora of research has addressed these changes by analysing couple and individual data, few have examined them within the context of multi-generational families. Using a case study analysis of a three-generation family, this paper shows that gender, class, social context, and family background influence how paid and unpaid work is divided within families. The case study shows that the social context of a given time conditions the options women and men have available to them in negotiating the balance of work and family responsibilities. Yet within this context, family background also matters. Negative childhood experiences were an impetus for adult children negotiating patterns of paid and unpaid labour that were different from those of their parents.
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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 | 0.002 | 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 | 0.000 | 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".