Bibliographic record
Abstract
Problem of so-called social dumping in EU law Abstract Social dumping in EU law is becoming an increasingly pressing problem. Due to the creation of the EU internal market and the accession of the new Member States, in particular southern and eastern Europe, social dumping is occurring in many forms in the European Union. Is it possible to prevent social dumping, or by what means can it be minimized? This diploma thesis in its first chapter deals with the definition of social dumping in EU law in general, focusing on social dumping in the form of so-called regulatory arbitrage, both theoretically and practically. In the second chapter, the author presents the development of social policy in the context of the EU internal market, since the interaction between the economic and social objectives of European integration is crucial for the existence of social dumping. This chapter is followed by the case law of the Court of Justice of the European Union in Rush Portuguesa, Viking Line, Laval, Rüffert and Commission v. Luxembourg, which concerns a conflict of fundamental freedoms and fundamental social rights. This chapter discusses how the Court of Justice of the European Union decides in the event of a conflict of these rights. Furthermore, that case-law has a considerable impact on the interpretation of the...
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".