Perceived fairness of the division of household labor: A comparative study in 29 countries
Bibliographic record
Abstract
This study investigates the relationship between the division of household labor and individuals’ perceived fairness concerning this division. We applied multilevel multinomial logistic regression to analyze data on both men and women across 29 countries using the International Social Survey Programme (ISSP) from 2012 ( N = 16,633). It was found that people who perform a larger share of household tasks are more likely to indicate that they do more than a fair share. Furthermore, we uncovered that in more gender egalitarian countries and in countries where women spend more time in the labor market, women and men are more likely to consider doing a larger share of housework to be unfair. Interestingly, when both country characteristics were included in the same model, we found that for women the effect of country’s female labor force participation lost statistical significance, while for men the country-level gender ideology resulted in a non-significant effect. Implications for future research are discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".