What’s integrity got to do with it? Second-year experiences of the Path2Integrity e-learning programme
Bibliographic record
Abstract
Organisations in Europe differ significantly in how they promote research integrity (RI). Higher education institutions play a pivotal role in disseminating a culture of RI and responsible conduct of research (RCR). Adhering and strengthening mentoring systems, implementing codes of conduct, and raising awareness are just a few initiatives among many to enhance students’ training in RCR. This article describes the Path2Integrity Learning Card (P2LIC) programme, a proactive training programme to foster RI. This programme was further developed in 2020 and the updated feedback loops took place in four countries (Germany, Denmark, Spain, and Poland). We outline the P2ILC development and final design, the trainer feedback on the programme from the second year of operation, and suggest future considerations for RCR training to strengthen research integrity.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".