From principles to protection: Leadership in uniting integrity and ethics for research security
Bibliographic record
Abstract
In today's global research landscape, ensuring integrity and security is critical. The CORRIE - Collaborative Open Resources on Research Integrity and Ethics - project brought together an international, multidisciplinary, cross-jurisdictional team to develop practical principles and resources for research integrity and ethics. Using a values-driven, collaborative leadership model, the team co-created tools grounded in authenticity and accountability, establishing an actionable framework. These resources should effectively address research security, fostering a resilient, effective research culture. Through a user-informed methodology and inclusive collaboration, the project team translated principles into tangible tools reinforcing ethical conduct in daily practice. This approach ensures robust protections without stifling crucial innovation. The CORRIE project offers notable societal value, bolstering research credibility and trustworthiness. It advances equity by providing shared standards for all researchers, clearly demonstrating that integrity, ethics, and security are interconnected and mutually reinforcing. This significant work meaningfully contributes to United Nations Sustainable Development Goals (SDGs) 4, 9, 16, and 17, fostering quality education, robust innovation, strong institutions, and effective partnerships.
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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 | 0.245 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.089 |
| Scholarly communication | 0.040 | 0.036 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.012 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".