Community-led approaches to research governance: a scoping review of strategies
Bibliographic record
Abstract
Around the world, a growing number of communities are voicing their demands for authority in the governance of research involving them. Many such communities have experienced histories of exploitative, stigmatizing, intrusive research that failed to benefit them. To better understand what strategies communities are developing in order to have a say in research oversight, we conducted a scoping review of the international peer-reviewed and grey literature. Three primary strategies were identified: (1) guidelines; (2) community review boards; and (3) community advisory boards. Guidelines include documents developed by, with, or for communities to outline ethical behavior or conduct in research with or within the community. Community review boards offer ethical review of research protocols, much like traditional research ethics boards, but are community led and focus on community interests. Community advisory boards consist of representatives from a given community and are developed to advise institutions or research teams on community-level ethical matters pertaining to research projects. Initiatives led by Indigenous communities far outnumbered others in the sample, reflecting the legacy of continuous Indigenous resistance to research as a tool of colonialism. In several cases, communities in marginalized neighbourhoods, where harmful and exploitative research practices have taken place, emphasized the significance of community-led governance grounded in shared geographical and social contexts. We discuss some of the beneficial and challenging features of each type of strategy and offer recommendations for stakeholders who wish to support community-led efforts in research ethics.
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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 | 0.181 | 0.280 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.053 | 0.051 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| 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".