Can Governments Control Mass Layoffs by Employers? Economic Freedoms vs Labour Rights in Case C-201/15 AGET Iraklis
Bibliographic record
Abstract
The AGET Iraklis case (C-201/15) revisits the Viking/Laval case law and sheds new light on the uneasy relationship between labour law and the EU’s fundamental economic freedoms. This article examines three sets of issues: the balance between the economic and the social in AGET Iraklis; the interplay between freedom to conduct a business (Article 16 of the EU Charter) and labour rights; and the Economic and Monetary Union dimension of the Court’s ruling in AGET Iraklis. The article makes three key claims. First, it is argued that the Court’s ruling marks a step towards a reconciliation between EU free movement law and labour law. Second, it is argued that Article 16 of the EU Charter of Fundamental Rights can be more ‘dangerous’ to labour rights when EU secondary law is interpreted in the light of that provision (such as in Alemo-Herron). In cases where both EU free movement law and Article 16 are engaged, the latter may not be equally influential. Third, it is noted that the margin of appreciation left to the domestic authorities might lead to further deregulation of the national labour law concerned, as Greece is subject to an economic adjustment programme.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.013 | 0.007 |
| 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".