Convergence toward the European Social Model? Impact of EU accession on Polish social policy
Bibliographic record
Abstract
In order to join the European Union (EU) Poland had to meet a wide range of conditions including adoption of acquis communautaire, significant administrative reforms and economic restructuring. This article deals with all these EU-membership commitments which directly influenced the Polish social policy, spanning such areas as free movement of persons (mainly workers), labour law, social dialogue, labour market and social inclusion policies and pensions. These changes - even if incremental and evolutionary - made the Polish welfare state more compatible with the European Social Model. Judging from the experience of Poland, the European Social Model (ESM) is far from vague and meaningless ideology. The ESM has had a significant impact on national social policies which is discernible at four general levels: values and general rules, which engender a welfare state philosophy shared by all Member States; Community-enforced social minimum standards; European-level institutional co-operative procedures; and monetary transfers in the framework of cohesion policy. The impact of the EU is visible to a varying degree – ranging from substantial in the peripheral areas such as gender equality or health and safety at work to purely theoretical in fiscal and monetary matters. The Polish welfare state has been heavily influenced by practical day-to-day administrative and institutional co-operation of Poland with the UE.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".