Education, work and welfare in diverse settings
Notice bibliographique
Résumé
Introduction The discussion in this chapter will look at how neoliberalism in Norway, Japan, Poland and Spain has influenced and shaped youth policy over the past twenty years. We will begin the analysis by focusing on the question of education and training, followed by an examination of the strategies that each country has developed for dealing with unemployment, work and welfare. The review will also show how the different states have been developing their post-16 education policies, highlighting the importance of the local context in how they are responding to the neoliberal agenda, especially since the 2007 crisis. We will also examine the significant differences in strategy not only between these four states but also in terms of how they vary with regard to the UK, Australia, Canada and New Zealand that were discussed in detail in the first part of the book. Post-16 education and training As we saw in Chapter Three, both the levels of participation and the number of qualifications that a young person gets have increased over the last fifteen to twenty years in all eight countries. Since the 2007 crisis, and throughout the recession, participation has continued to expand. However, differences continue to exist not only in the level of participation but also in how education and training is provided. In Norway and Spain, education is funded substantially from public funds, while in Japan education is run and managed fundamentally by the private sector. Poland, in its adjustment to a new European state, has created a partnership between public and private providers. One key feature in all eight countries is that young people's level of engagement in post-16 education is strongly influenced by what is happening to employment opportunities, although local factors also make a difference. This is clearly evident when considering Norway, Spain, Poland and Japan, and there are some interesting trends. In Norway, when the young were able to access good quality jobs (between 2000 and 2007), their level of involvement in education declined, but this changed in 2008 (OECD, 2014a). In Spain, the relationship between unemployment, work and local factors is more complex. The proportion of young people involved in post-16 education after the Franco period (in the 1970s) until 2007 was one of the lowest in Europe and the Organisation for Economic Cooperation and Development (OECD) countries.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,001 |
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 ».