Bibliographic record
Abstract
The article analyzes work—family balance among working couples in 29 countries using data from ISSP 2002. Arguments derived from theories on family regimes and modernization are tested. The results indicate that respondents can be categorized into three clusters. The first comprises those having a work—family balance; the second, those having an occupational work-overload; and the third, those having a dual work-overload (i.e. those experiencing too strong demands from both work and family responsibilities). Across countries, cluster sizes vary tremendously. The results indicate that the wealth of a country is strongly associated with the likelihood of achieving a balanced work—family situation. Although the overall probability increases with economic wealth, the relative disadvantage for women compared to men persists. The female disadvantage is mainly a higher risk of occupational overload in the rich countries, whereas in poorer countries there is a higher risk of being in a dual work-overload situation. Among the wealthy industrialized democracies, a balanced work—family situation is more common in the familialist German-linguistic country grouping, followed by the Nordic countries characterized by de-familialization. Market-oriented countries perform less well. Within the perspective of the theory on family regimes, the similarity between the familialist and the de-familialist regimes is an unexpected result.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".