Mapping nine decades of research integrity studies (1935–2024): A scientometric analysis
Bibliographic record
Abstract
BACKGROUND: Research integrity is fundamental to responsible research practice. Despite attention, the intellectual structure and evolution of this field remains underexplored. This study maps the knowledge landscape of research integrity, identifying key themes, contributions, and trends. METHODS: A scientometric analysis was conducted on 6,895 records from Web of Science and Scopus (1935-2024). CiteSpace facilitated network analysis, including co-authorship, keyword co-occurrence, and co-citation patterns, while burst detection identified topics. RESULTS: Research integrity studies have grown significantly since the 1980s, with interdisciplinary collaboration. Keyword and co-citation analyses reveal a shift from early discussions on scientific misconduct to concerns such as open science, AI ethics, and research governance. A collaboration network has emerged, with leading contributions from North America, Europe, and Asia. CONCLUSIONS: Research integrity has matured into an interdisciplinary field, reaching academic consensus with growing integration of policies, regulations, and technology. Future research is expected to focus on AI's role in research integrity. Key areas of concern include algorithmic bias, automation ethics, and implications for scholarly publishing. Open science and transparency will remain central, particularly in addressing data fabrication, paper mills, and predatory publishing. Institutional policies will continue evolving, embedding integrity principles into governance and public engagement initiatives.
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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 | 0.029 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.097 | 0.150 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".