Meaningful public involvement: changing research institutions toward epistemic justice
Bibliographic record
Abstract
Public and patient involvement and engagement (PPIE) in research is increasingly expected and often formally required by the sponsors. However, creating and sustaining conditions for meaningful PPIE can be challenging. It requires efforts of all involved parties. While much attention is given for the ethical inclusion of individuals as research participants, their collaboration with researchers and design of accessible research processes, there is a question of how research institutions can support PPIE. We argue for comprehensive changes within research institutions to facilitate meaningful PPIE practice. These changes should include institutional culture and attitudes toward the public members involved in research, to foster meaningful encounters between people with different forms of knowledge and life experience, such as professionally trained researchers and members of marginalized social groups. In this study, we propose a framework of institutional changes for PPIE, which focuses on their sociocultural and epistemic features. We explore the context of PPIE and possible risks related to disregarding public members as owners of valid knowledge. We use the order of change model as a frame and emphasize the role of third-order changes, which involve raising awareness about diverse forms of knowledge. Such changes would allow for sustaining PPIE research as knowledge space, wherein public members and researchers can respectfully share knowledge to inform scientific inquiries. Based on these conceptualizations, we outline practical examples and future directions. Better conceptualizing of institutional changes can contribute to facilitating their implementation and thereby more ethical research practice.
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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 | 0.159 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.073 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.005 | 0.046 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".