Bibliographic record
Abstract
Increasing attention is being paid to the impacts of country-level contexts on the work-life interface. However, lack of theoretical clarity as well as operationalization challenges are significant roadblocks for comparative work-life research. This article provides guidance for cross-national work-life research by conducting a systematic interdisciplinary review of conceptual and empirical work on the country-level cultural impacts (i.e., the values, assumptions, and beliefs shared by individuals with common historical experience) and structural impacts (i.e., the rules and constraints produced by legal, economic, and social structures) on individual experiences of the work-life interface and organizational support for nonwork. Regarding culture, we offer an organized review of work-life research drawing on cultural dimensions, from the most researched dimensions, such as individualism-collectivism and gender egalitarianism, to the least researched. We also point to ways to locate scales and country scores. Concerning structure, we explain how legal (e.g., public policies), economic (e.g., industrialization), and social (e.g., actual gender equality) factors are operationalized with indicators or typologies and review the related work-life research. We carve out a research agenda pointing out untapped cultural dimensions and structural factors and underresearched work-life constructs and calling for more systemic and integrative cross-national work-life research.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".