Fathers on parental leave: an analysis of rights and take-up in 29 countries
Bibliographic record
Abstract
Using information published in 2014 annual review of the International Network on Leave Policies and Research, the article analyses parental leave and benefit policies in 29 countries to identify which characteristics can potentially facilitate fathers’ take-up of parental leave. The scarce statistics that is available shows that only few countries have been successful in increasing fathers’ participation in the parental leaves, despite the fact that some recent policy schemes seem to have drawn lessons from the Nordic success. There are several countries which indeed have adopted principles similar to the Nordic countries in their leave schemes, such as fathers’ quota, generous income-related benefit or long duration of the leave. The evidence suggests that only taking over some elements of the successful policy schemes does not necessarily lead to a change in the leave-taking behaviour of fathers and families. The evidence shows reasonably high take-up of parental leave only in countries where there is a combination of fathers’ quota and high level of benefit. There is still no evidence to confirm that replicating the fathers’ quota in its Nordic designs other societies would generate similar behavioural change as it did in the Nordic countries.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".