Early lessons from the International Study of Work-Family Experiences
Bibliographic record
Abstract
The International Study of Work-Family Experiences (ISWAF) is a large-scale comparative cross-national study that was recently set up and is being launched in 35 countries across the globe. ISWAF aims at analyzing the impact of national context and in particular cultural values on individuals’ work-family conflict, enrichment, and balance, as well as on the management of boundaries between work and family roles. The panel aims at sharing the on-going experiences of scholars contributing to ISWAF, for the benefit of all those interested in cross-national work-family research and in the challenges of designing and coordinating large-scale comparative surveys. First, Barbara Beham will share early lessons learned from the setting up of ISWAF (survey design and collaborators’ network). Second, Ameeta Jaga and Artiawati Mawardi will analyze their experiences collecting data in South Africa and Indonesia, respectively. Third, Ariane Ollier-Malaterre will discuss how ISWAF is attempting to capture sub-cultures and within-country heterogeneity, in the Canadian context and beyond. Fourth, Suzan Lewis will explore how ISWAF could be used in tandem with qualitative approaches, or serve as a first step for emic in-depth studies. Last, Andreas Baierl will put forth ideas regarding data analysis strategies most likely to be relevant for the multi-level data being collected. \nDiscussants: \n•\tTammy Allen, University of South Florida \n•\tAriane Ollier-Malaterre, Université du Québec à Montréal (UQAM) - École des Sciences de la Gestion (ESG) \nPresenters: \n•\tBarbara Beham, Berlin School of Economics and Law; \n•\tAmeeta Jaga, University of Cape Town; \n•\tArtiawati, University of Surabaya; \n•\tSuzan Lewis, Middlesex University; \n•\tAndreas Baierl, University of Vienna; \n \n
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".