Education, work and welfare in diverse settings
Bibliographic record
Abstract
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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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 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".