Divergence and difference: contrasting cross-national experiences of being young
Bibliographic record
Abstract
Introduction In this chapter we begin by outlining how Norway, Japan, Poland and Spain have been encountering and engaging with neoliberalism. We will also explore what impact the 2007 crisis and the great recession that followed has had on these countries, and how it has influenced their relationship with neoliberal approaches to policy and practice. As in Chapter Two, the analysis will show how each of these states has evolved and how they have interacted with neoliberalism historically. It will also outline the impact of the crisis and how, at a macro level, public policy has responded. The chapter will then explore the implications of what it means to be young in these four countries, highlighting the way social citizenship has been constructed in each of the nation states. In this process we will see that what it means to be young in Norway, Japan, Poland and Spain not only varies between the different countries but also in comparison to the other four case study areas discussed previously. Ideas of citizenship and ‘being young’ are shaped by the different contexts in which they are constructed. In Chapter Two we identified four key trends that were evident across the UK, Australia, Canada and New Zealand. These were: • The growing and expanding role of neoliberalism and neoliberal ideology in shaping the policy agenda of the nation state. While the historical trends were not always linear or unresisted, it was evident that neoliberalism has been having a significant impact in the UK, Australia, Canada and New Zealand. • This drive towards neoliberalisation was not just driven by the political right – in fact the ‘third way’ politics of the left was fundamentally built around the principles of neoliberalism. When the political left were in office they did not reject or repeal neoliberal policies but tended to ‘tinker’ with them to soften the social impact. • Throughout the 2000s there was a strong emphasis on ‘rolling out’ and embedding market principles into public services. Quasi markets and public–private partnerships for delivery became the norm. • Neoliberalism is not just an economic project; it has embedded within it a strong moralising agenda, one that aims to shift responsibilities (and costs) towards the individual.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".