ENCJ Survey among judges on the independence of the judiciary 2025
Bibliographic record
Abstract
The survey among the judges of Europe about their independence took place for the fifth time in the first quarter of 2025. In total 19,136 judges from 32 judiciaries of 30 countries participated. The target for participation was set at 20%, which most judiciaries (easily) achieved. The main findings are: (1) Judges generally evaluate their independence positively. On a 10-point scale, judges rate the independence of the judges in their country on average between 5.9 and 9.8 with the lowest score for Ukraine, followed by Montenegro (6.8), Hungary (7.0), Bulgaria and Bosnia and Herzegovina (both 7.1). The scores of ten judiciaries are 9 or higher. The respondents rate their personal independence even higher: between 6.8 and 9.9. Consistent with the positive assessment of independence, few judges report inappropriate pressure to influence judicial decisions. (2) Since 2015, when the first survey took place, independence has gradually improved on average for all judiciaries together. However, this trend comes to a halt in this survey, where depending on the yardstick the average score across countries remained the same or declined somewhat since the previous survey. Based on the experience of judges who have been working for many years, independence has improved over a longer period. (3) Examining the judiciaries individually, in most of them perceived independence remained high or improved since the first survey. However, in some judiciaries the respondents see declines. This is the case in Hungary which participated for the first time in 2019, but also in Montenegro and Greece (foremost civil and criminal courts) declines occurred and to a lesser extent in Slovenia. In Bosnia and Herzegovina the independence score is stable at a low level.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".