ENRIO 2025: Congress on Research Integrity Practice. Research Integrity, Power Dynamics and Safe Institutional Culture
Bibliographic record
Abstract
The ENRIO 2025 Congress in Ljubljana continued the series of biennial events addressing research integrity practice (RI) and the development of responsible research in Europe. The first ENRIO Congress, initiated by the former ENRIO Chair Sanna Kaisa Spoof (TENK, Finland), took place in Helsinki in 2021 and faced the unique challenge of being held in a hybrid format due to the pandemic. The 2nd ENRIO Congress (2023) was co-organized by OFIS and hosted by the Sorbonne University in Paris. While Ljubljana, and in particular the University of Ljubljana, was open to coorganize the 3rd ENRIO Congress. We were ultimately able to welcome over 250 participants, from 35 countries across all continents, to Ljubljana. The increased interest reflects a growing awareness of the relevance of research ethics and integrity, as well as the ever more significant role of ENRIO.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".