The politics of parental leave policiesChildren, parenting, gender and the labour market
Bibliographic record
Abstract
Introduction ~ Peter Moss and Sheila B. Kamerman Australia: the difficult birth of paid maternity leave ~ Deborah Brennan Canada and Quebec: two policies, one country ~ Andrea Doucet, Lindsey McKay and Diane-Gabrielle Tremblay Czech Republic: normative or choice-oriented system? ~ Ji?ina Kocourkova Estonia: halfway from the Soviet Union to the Nordic countries ~ Marre Karu and Katre Pall Finland: negotiating tripartite compromises ~ Johanna Lammi-Taskula and Pentti Takala France: gender equality a pipe dream? ~ Jeanne Fagnani and Antoine Math Germany: taking a Nordic turn? ~ Daniel Erler Hungary and Slovenia: long leave or short? ~ Marta Korintus and Nada Stropnik Iceland: from reluctance to fast-track engineering ~ Thorgerdur Einarsdottir and Gyda Margret Petursdottir The Netherlands: bridging labour and care ~ Janneke Plantenga and Chantal Remery Norway: the making of the father's quota ~ Berit Brandth and Elin Kvande Portugal and Spain: two pathways in Southern Europe ~ Karin Wall and Anna Escobedo Sweden: individualisation or free choice in parental leave ~ Anders Chronholm The European Directive: making supra-national parent leave policy ~ Bernard Fusulier Conclusion ~ Sheila B. Kamerman and Peter Moss.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".