Is Justice Contextual? Married Women’s Perceptions of Fairness of the Division of Household Labor in 12 Nations
Bibliographic record
Abstract
Distributive justice theory argues that individuals make fairness decisions partly by comparing themselves to similarly situated others. Utilizing fixed effects analyses of 4,643 married women nested in 12 nations from the l 991 International Social Justice Project, this paper examines whether the relationship between the division of household labor and perceptions of fairness of that division was informed by such comparisons. Specifically, this paper tests whether political history, women’s political representation, and average division of labor within a nation set expectations for the division of household labor against which married women made fairness determinations. Although political history and women’s political representation seem to have been directly influential in constructing fairness determinations in 199 1, married women did not seem to use the expectations of (in)equality as comparison referents. The normative division of labor within a nation did not directly or indirectly influence perceptions of fairness. Nations provide a context within which married women make fairness determinations; setting expectations does not seem to have been the mechanism through which context has historically operated. Additional cross-national research on this topic is warranted.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".