Ronald R.Rindfuss and Minja KimChoe (Eds.) Low and Lower Fertility: Variations across Developed CountriesSpringer, 2015. 196 p. $129.00; $122.55 (pbk).Ronald R.Rindfuss and Minja KimChoe (Eds.) Low Fertility, Institutions, and their Policies: Variations across Industrialized CountriesSpringer, 2016. 303 p. $99.99.
Notice bibliographique
Résumé
Over recent decades fertility throughout the developed world has declined to below the replacement level, with fertility in some countries falling to one birth per woman. This trend has become a concern for policymakers primarily because low fertility is resulting in rapid aging of populations. Aging in turn threatens the viability of social security and health care systems, which for the most part rely on pay-as-you-go financing, i.e., the contributions of the working-age population. These two volumes contain the revised papers from two workshops conducted in December 2013 and August 2014 and organized by the Korean Institute for Health and Social Affairs and the East-West Center. The first volume includes chapters on Australia, China, Hong Kong, Japan, South Korea, Netherlands, Singapore, and the United States. The second covers Austria, Canada, Czech Republic, France, Hungary, Italy, Norway, Spain, Taiwan, and the United Kingdom. While this set of countries is representative of the developed world, a few large countries—in particular Germany and Russia—are excluded. Each chapter is written by a country expert who presents recent fertility trends and discusses the social, economic, institutional, historical, and cultural factors affecting fertility as well as policies. In addition to the country chapters, each volume contains an introductory chapter by the editors that summarizes the key themes that run through the volume, including labor market factors, child care availability, parental leave, gender equity, education system, availability of housing, and government subsidies. A final chapter provides a cross-country comparison of individual perceptions about obstacles to having children, based on survey data, and government support for families. The central puzzle the authors address is the lack of a simple explanation for the diversity of fertility levels (ranging from 2.1 to 1). The main message is that a wide range of factors affect fertility positively or negatively and that this multiplicity of factors differs from country to country, yielding a unique net outcome. Any given level of fertility can be reached through different pathways. Additional complexity results from variation in causes over time. Government policy can affect fertility levels through a wide range of subsidies and regulations, although effects of specific interventions tend to be small. The role of government as measured by the percent of GDP devoted to family benefits ranges from 3.5 percent in Nordic countries to less than 1.5 percent in Southern Europe, Japan, South Korea, and Taiwan. It is not surprising that the level of public investments in families correlates positively with the level of fertility. The two volumes are an invaluable resource for students, researchers, and policymakers, providing comprehensive and highly informative expositions of the causes of low fertility in high-income countries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,033 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».