Introduction: comparing carer leave policies cross-nationally
Bibliographic record
Abstract
This book examines and compares, for the first time, the origins, content and implications of national policies and policy instruments intended to enable people to provide care to family members and friends while remaining in paid employment.It focuses particularly on the emergence of legislated opportunities to take temporary leaves of absence from work to permit employees to fulfil care responsibilities -referred to throughout as 'carer leave'.In all countries included in this comparative book -Australia, Canada, Finland, Germany, Japan, Poland, Slovenia, Sweden and the United Kingdom -the proportion of employees with caring responsibilities is growing.Population ageing and global trends in economic conditions, among other factors, have produced rising labour force participation rates, especially among women, and dual-earner households have increasingly become the norm (Ortiz-Ospina et al, 2018).In many countries across the globe, although life expectancy is increasing, disability-free and healthy life expectancies have remained stable, increasing demand for care (Beard et al, 2016; GDB Ageing Collaborators, 2022).Health and long-term care systems are presented as being 'in crisis' due to concerns about financial and social sustainability (UN, 2018).In this context, many governments around the world are seeking to reduce or minimise expected growth in expenditure on health and care services.Increasingly, some argue, national policies rely ever more heavily on carers, with states 'bringing the family in [to caring arrangements] through the back door' (Kodate and Timonen, 2017: 291).As a result, there is now substantial evidence that family members and friends are providing high levels -and indeed the vast majority -of care around the world (Cès et al, 2019;Dykstra and Djundeva, 2020;Fast et al, 2023).Carers, it is argued, are conceived of as 'background resources' within welfare systems (Lloyd, 2023: 134), with their own needs and well-being marginalised.Reflecting these trends, the number of 'working carers' has grown rapidly and is predicted to continue to rise as societies age (Addati et al, 2018;Wimo et al, 2018).As Bouget et al (2016) highlight, demographic, social and cultural trends are increasing the likelihood of having to combine paid
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".