Collaborative Open Resources on Research Integrity and Ethics (CORRIE)
Bibliographic record
Abstract
This course has been collaboratively developed by staff of Munster Technological University (MTU), Atlantic Technological University (ATU) in Ireland, the University of Windsor in Ontario, Canada, and Teagasc. It was originally funded as part of the N-TUTORR project at MTU, with follow-up funding from SATLE Reusable Learning Resources. The Articulate Rise 360 platform has been used to build a series of asynchronous multi-media self-contained learning objects and further support compliance of researchers (students and staff) on research integrity and research ethical requirements locally, nationally and internationally. The resources entail media-rich components consisting of interactive documents, audio and video files, and self-testing opportunities through practical scenarios and dilemmas encountered in the areas of research integrity and research ethics. The resources are designed to align with the principles of Universal Design Learning and Open Science. The resource content is framed around the lessons and includes: - Principles of Research Integrity, - Principles of Research Ethics, - Vulnerability and Vulnerable Groups in Research, - Evidence-Informed Practice, - Community/Participant Engagement, - Informed Consent, - Voluntary Participation and Right to Withdraw, - Data Storage and Management, - Responsible Dissemination, - Justification for Animal Research, - Animals as Sentients, - The 3 R’s Principle, - The 5 Domains Model for Assessing Animal Welfare, - Legal Oversight and Policy Regulations in Animal Research, - Ethical Challenges and Public Perception in Animal Research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.167 | 0.071 |
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".