Redistribution, Inequality and Nordic Welfare States: Challenges in a Global World
Bibliographic record
Abstract
Abstract As elsewhere, inequality has increasingly been on the agenda in recent years in Denmark, which has led to discussion about the redistributive role of welfare states across existing welfare regimes. Perhaps surprisingly, the Danish debate on inequality has revolved more specifically around how the country's tax system influences labour supply, especially the high level of marginal income taxation. The debate on poverty and inequality has become more prominent in Denmark in recent years, with a focus on the living standards of pensioners and children as well as the dynamic relationship between inequality and social policy. Thus, if there is a willingness to reduce inequality, a central challenge is to determine which instruments are available to counter rising inequalities in Denmark. In this context, the interaction between the issue of poverty and political support for specific social policies in Denmark is a challenge. Overall, the analysis suggests that tax reforms focusing on labour market supply have helped increase inequalities, thus indicating a possible trade-off between different aspects of welfare state development. Furthermore, the universality of the Danish model might be questioned in the coming years, which might also imply a debate on the generosity of a number of social security benefits, including those targeting the unemployed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".