Bibliographic record
Abstract
Social cohesion is one of the declared objectives of the European Union and, with some 16% of EU citizens at risk of poverty, the need to fight poverty and social exclusion continues as a major challenge. This book provides an in-depth analysis of the EU Social Inclusion Process, the means by which it hopes to meet this objective, and explores the challenges ahead at local, regional, national and EU levels. It sets out concrete proposals for taking the Process forward. The book provides a unique analysis of policy formulation and assessment. Setting out the evolution and current state of EU cooperation in social policy, it examines what can be learned about poverty and social exclusion from the EU commonly agreed indicators. Taking the position of outside, but informed, observers, the authors explore the further development of the common indicators, including the implications of Enlargement, and consider the challenges of advancing the Social Inclusion Process - strengthening policy analysis, embedding the Process in domestic policies and making it more effective. Proposing the setting of targets and restructuring of National Action Plans and their implementation, they emphasise the need for widespread ownership of the Process at domestic and EU level and for it to demonstrate significant progress in reducing poverty and social exclusion. The book will be invaluable to academics, students and policy-makers at sub-national, national and EU levels as well as to social partners, and NGOs working towards a more inclusive society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".