Changing inequalities in rich countries: analytical and comparative perspectives
Bibliographic record
Abstract
Captures and investigates inequality trends in income, wealth, education, and the labour market. Provides detailed information on inequality experiences across 30 countries examining trends over 30 years. Combines statistically sophisticated comparative analysis with evidence from individual countries experiences. Complements the volume 'Changing Inequalities and Societal Impacts in Rich Countries: Thirty Countries' Experiences'. There has been a remarkable upsurge of debate about increasing inequalities and their societal implications, reinforced by the economic crisis but bubbling to the surface before it. This has been seen in popular discourse, media coverage, political debate, and research in the social sciences. The central questions addressed by this book, and the major research project GINI on which it is based, are: - Have inequalities in income, wealth and education increased over the past 30 years or so across the rich countries, and if so why? - What are the social, cultural and political impacts of increasing inequalities in income, wealth and education? - What are the implications for policy and for the future development of welfare states? In seeking to answer these questions, this book adopts an interdisciplinary approach that draws on economics, sociology, and political science, and applies this approach to learning from the experiences over the last three decades of European countries together with the USA, Japan, Canada, Australia, and South Korea. It combines comparative research with lessons from specific country experiences, and highlights the challenges in seeking to adequately assess the factors underpinning increasing inequalities and in identify the channels through which these may impact on key social and political outcomes, as well as the importance of framing inequality trends and impacts in the institutional and policy context of the country in question.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".