In the Grips of Work/Family Imbalance: Local and Migrant Domestic Workers in Slovenia
Bibliographic record
Abstract
Gender equality policies determine the inclusion of women into the labor market as a fundamental indicator of equality between men and women — women should participate in the labor market in the same way as men do. In the reconciliation debates between work and family life, women’s labor market participation sometimes uncritically associates paid work with the success and self-fulfillment of the careers of affluent women, while marginalizing the experiences of working-class women and overshadowing problems of inequalities in work/life balance among women with various statuses and experiences. As Peterson (2011, 52) argues, the dominant discourses of reconciliation privilege some women over others, and policies on work/family balance put forward an exclusionary vision of gender equality, defining it as equality only for “white”, middle-class, heterosexual mothers in dual-career families; other women (older women, working-class and migrant women, single mothers) are marginalized in the reconciliation policy debates. Peterson, therefore, accentuates the importance of analyzing reconciliation issues as intertwined with multiple intersecting inequalities according to class, ethnicity/race, and migrant background. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".