Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
This thesis consists of four self-contained empirical studies, with three underlying themes: Migration, Education and Culture. First, using annual data over the period 1820-2010, Chapter 2 examines the productivity effects of immigrants’ traits on growth in Argentina, Australia, Brazil, Canada, New Zealand and the United States. Immigrants’ traits such as wealth, culture, institutions, R&D knowledge, and education are traced back to their country of origin. Culture is found to be consistently the most important productivity-enhancing trait of immigrants, followed by education. Second, using annual data over the period 1850-2010, Chapter 3 examines the impact of immigration as well as the immigrants’ educational and cultural background on unemployment in Argentina, Australia, Brazil, Canada, New Zealand and the United States. The results show that immigrants lowered unemployment before WWII but not thereafter and that immigrants from Protestant countries have lowered unemployment throughout the entire period, 1850-2010. Third, using panel data on the eight Australian states and territories, Chapter 4 examines the effects of migration on house prices in Australia from 1971-2013, accounting for both international and internal migratory movements. The results show that migration driven population growth has a significant effect on house prices in the short-run and that inter-state migration needs to be account for due to the large inter-state movements. Overall, the other results are relatively consistent with existing literature. In the short-run, housing prices are significantly driven by inertia in house prices, interest rates, the unemployment rate and income. In the long run however, house prices are driven by their replacement costs, measured by construction costs. Last, using panel data of 21 OECD countries from 1820 to 2009, Chapter 5 seeks to explain the mass rise in education we have witnessed in the last few centuries. Specifically, it examines the impact of government regulation in the form of compulsory schooling laws and child labour laws, culture, life returns to education, structural changes in the economy and the sequential nature of schooling on school enrolment rates – primary, secondary and tertiary. Results suggest that primary and secondary schooling is significantly influenced by government regulation on schooling and that more liberal cultural values, higher life expectancy and an expansion of the knowledge intensive sector have a positive effect on enrolment across the three levels of schooling.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle