Social Policy and Family Assistance
Bibliographic record
Abstract
Slovenia’s social protection system is well developed and social spending has a large impact on redistribution of resources, reducing the risk of poverty by half. Overall spending on social policy amounts to about a quarter of GDP which is around the OECD average. With economic growth and favourable employment trends, the number of social assistance beneficiaries went down. Although recent reforms aimed at activating unemployed people were implemented, they did not fully succeed. The participation of social assistance recipients in active labour market programmes remained low, at least initially. Hence increasing the activation of welfare recipients remains a policy challenge, all the more in time of economic slowdown. Family policy is well developed, and includes a wide array of child benefits, parental leave and maternity leave allowances, and financial support towards childcare and kindergartens. On the whole childcare provisions are comparable with the OECD average and in terms of work and family outcomes, Slovenia generally scores well in international comparison. However, fertility rates are currently well below the replacement level. The reasons behind this low fertility rate can be found in difficulties young people encounter in getting established in stable employment as well as in difficulties they face in moving out of the parental home because of housing costs. Social transfers have relatively broad coverage in Slovenia and social spending appears to have a high effectiveness with regard to poverty and inequality reduction. Nevertheless, the tax burden on low-wage work is high in Slovenia compared with other OECD countries with well developed outof- work support systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".