Cross-national work–life research: common misconceptions and pervasive challenges
Bibliographic record
Abstract
While cross-national work-life research is a flourishing field of research, but a recent one, it is relatively recent as national context had been the missing ‘elephant in the room’ of work–life research for decades. Based on the keynote talk I gave at the 2015 Community, Work and Family conference in Malmö, Sweden, this research note highlights three pervasive challenges which I believe need to be discussed in our community of scholars, practitioners and policy-makers so that our research makes the strongest possible impact for individuals and organisations: (1) educating practitioners and policy-makers on the structuring impact that public and employer policies have on individual so-called private work–life decisions; (2) analyzing the inequalities of access to these policies within each country, which are often masked by simplified country-level comparisons and (3) finding innovative ways to combine ambitious etic research designs with in-depth emic understanding of local cultures.
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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 | 0.323 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.013 | 0.101 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".