The Slow Road to the Social Investment Perspective in the European Union
Bibliographic record
Abstract
The European Union has no unilateral legislative capacity in the area of social policy. However, the European Commission does play the role of guide by providing a discursive framework and targets for its 28 Member States to meet. Since the late 1990’s, the EU’s ideas on social policy have moved away from the traditional social protection model towards promoting social inclusion, labour activation and investing in children. These new policies represent the social investment perspective, which advocates preparing the population for a knowledge-based economy to increase economic growth and job creation and to break the intergenerational transmission of poverty. The EU began the gradual incorporation of the social investment perspective to its social dimension with the adoption of ten-year strategies. Since 2000, it has continued to set goals and benchmarks as well as offer a forum for Member States to coordinate their social initiatives. Drawing on a series of interviews conducted during a research experience in Brussels as well as official documents, this paper is a descriptive analysis of the recent modifications to the EU’s social dimension. It focuses on the changes created by the Europe 2020 Strategy and the Social Investment Package. By tracing the genesis and evolution of these initiatives, the author identifies four obstacles to social investment in the European Union's social dimension.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".