Artificial Intelligence Tools Usage Policy at the University of Rijeka, Croatia
Bibliographic record
Abstract
Supporting scientific progress and knowledge dissemination by adopting innovative technologies and models for their responsible application, the University of Rijeka advocates a responsible, ethical, open, transparent, and innovative approach to the usage of AI tools and other advanced digital technologies. In this framework, the University of Rijeka Council of Honor, at its session held on April 18, 2023, issued a Statement on the responsible usage of artificial intelligence tools, emphasizing the need for a constructive discussion about a responsible, ethical, and transparent usage of AI tools with the staff and students of the University, and welcoming all institutional and individual efforts towards the responsible usage of AI tools in learning and teaching, including their permitted, critical, and informed usage while respecting the highest ethical principles. Following this statement, in this policy document, adopted by the Senate, the University of Rijeka further elaborates the basic terms, defines the aims of AI tools usage in teaching and research, states the fundamental principles of their usage at the University, defines the activities of the University in this area as well as the stakeholders involved in these activities. Hence, the University expresses its determination to maximally use the advantages and potentials of new AI-based technologies, while at the same time taking into account the highest ethical standards by promoting continuous education and developing critical thinking of all included stakeholders.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".