Bibliographic record
Abstract
Chapter 1 Family policies in low fertility countries 1 (Vinod MISHRA, Chief, Policy Section, United Nations Population Division) Chapter 2 Aging Italy: low fertility and societal rigidities 31 (Maria Letizia TANTURRI, Assistant Professor in Demography, Department of Statistical Sciences, University of Padova) Chapter 3 The policy context of fertility in Spain: towards a gender-egalitarian model? 53 (Pau BAIZAN MUNOZ, Research Professor, Department of Political and Social Sciences, ICREA and Pompeu Fabra University) Chapter 4 Population Aging in the UK: A Matter of Perspective 97 (Wendy SIGLE-RUSHTON, Professor, London School of Economics and Political Science Gender Institute Chapter 5 Fertiliy decline and lasting low fertility in a continuously changing(policy) environment: a Hungarian case study 141 (Zsolt SPEDER, Director, Hungarian Demographic Research Institute) Chapter 6 Population Aging, Below-replacement Fertility and Population Policies since 1990 in Taiwan 199 (Melin LEE, Associate Professor, Department of Social Work, Asia University) (Yu-Hsuan LIN, Senior Specialist, Health Promotion Administration, Ministry of Health and Welfare) Chapter 7 Low fertility in Austria and the Czech Republic: Gradual policy adjustments 261 (Tomas SOBOKA, Wittgenstein Centre, International Institute for Applied Systems Analysis, IISA) Chapter 8 The relatively high fertility in Norway: a result of affluence, liberal values, gender-equality ideas, and the welfare state 353 (Oystein KRAVDAL, Professor, Department of Economics, University of Oslo) Chapter 9 Canadian Fertility Trends and Policies: A story of provincial variation423 (Sarah BRAUNER-OTTO, Assistant Professor, Department of Sociology, McGill University) Chapter 10 The influence of family policies on fertility in France: lessons from the past and prospects for the future 457 (Olivier THEVENON, National Institute for Demographic Studies ) Chapter 11 Value of Women’s Work at Home and Intergenerational Resource Allocation in South Korea 503 (Namhui HWANG, Fellow, Korea Institute for Health and Social Welfares) (Sang-Hyup LEE, Professsor, Department of Economics, University of Hawaii at Manoa) Chapter 12 Governmental support for families and obstacles to fertility 543 (Anne GAUTHIER, Senior Researcher, Netherlands Interdisciplinary Demographic Institute)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.207 | 0.100 |
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".