A structural model for explaining member state variations in preliminary references to the ECJ
Bibliographic record
Abstract
When a case before a domestic court gives rise to EU law questions, this court may (and sometimes must) ask the European Court of Justice to rule on the correct answer. The number of these preliminary references varies considerably between Member States. We set out to design a structural model that allows us to explain these variations. We base the model on the preliminary reference system which allows us to identify the structural and behavioural factors that form a pre-condition for a preliminary reference. Since observable data defining these factors does not exist, we further transform them into sub-components, thereby enabling us to identify proxies such as the size of the general government expenditure on law courts and the duration of EU membership. We perform statistical analyses of the associations between these proxies and the number of preliminary references. On this basis, we find that structural differences may explain about 85 per cent of the variation in preliminary references between the Member States, whereas at most the remaining15 per cent can be attributed to differences in judges’ behaviour.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".