Exploring the Dimensions of Responsible Research Systems and Cultures: A Scoping Review
Bibliographic record
Abstract
The responsible conduct of research is foundational to the production of valid and trustworthy research. Despite this, our grasp of what dimensions responsible research (RR) might contain – and how it differs across disciplines (i.e., how it is conceptualized and operationalized) – is tenuous. Moreover, many initiatives related to developing and maintaining RR are developed within disciplinary and institutional silos which naturally limits the benefits that RR practice can have. To this end, we are working to develop a better understanding of how RR is conceived and realized, both across disciplines, and across institutions in Europe. The first step in doing this is to scope existing knowledge on the topic, of which this scoping review is a part. We searched several electronic databases for relevant published and grey literature. An initial sample of 715 articles was identified, with 75 articles included in the final sample for qualitative analysis. We find several dimensions of RR that are underemphasized or are excluded from the well-established World Conferences on Research Integrity Singapore Statement on Research Integrity (WCRI) and explore facets of these dimensions that find special relevance in a range of research disciplines.
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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 | 0.102 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".