The Principle of Proportionality in <i>Viking</i> and <i>Laval</i> : An Appropriate Standard of Judicial Review?
Bibliographic record
Abstract
Although the application of the proportionality principle is a well known element of the ECJ's internal market case law, its application can raise very sensitive issues in an industrial action context. The argument of the present paper is that one of the main reasons why the outcome of the Viking and Laval cases was so controversial was because of the way in which the Court applied the proportionality principle. On the basis of some selected case studies from a labour law and industrial action context, the article will point out that with a better structured proportionality analysis the Court could have significantly improved the legitimacy of its decisions.
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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 | 0.108 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".