Research Integrity definitions and challenges
Bibliographic record
Abstract
Research integrity is guided by a set of principles to ensure research reliability and rigor. It serves as a pillar to uphold society's trust in science and foster scientific progress. However, over the past 2 decades, a surge in research integrity concerns, including fraudulent research, reproducibility challenges, and questionable practices, has raised critical questions about the reliability of scientific outputs, particularly in biomedical research. In the biomedical sciences, any breaches in research integrity could potentially lead to a domino effect impacting patient care, medical interventions, and the broader implementation of healthcare policies. Addressing these breaches requires measures such as rigorous research methods, transparent reporting, and changing the research culture. Institutional support through clear guidelines, robust training, and mentorship is crucial to fostering a culture of research integrity. However, structural and institutional factors, including research incentives and recognition systems, play an important role in research behavior. Therefore, promoting research integrity demands a collective effort from all stakeholders to maintain public trust in the scientific community and ensure the reliability of science. Here we discuss some definitions and principles, the implications for biomedical sciences, and propose actionable steps to foster research integrity.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.328 | 0.452 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.015 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".