Community-based research decision-making: Experiences and factors affecting participation
Bibliographic record
Abstract
From the post World War II period through to the present, scientific research and policy has increasingly reflected acceptance and implementation of a view that public interests are better served through public participation. Built on principles of democratic participation, community-based research (CBR) can produce new knowledge through the integration of knowledge of community members’ lived experience with the scientific and technical knowledge of academics. Although community-based research has experienced considerable recent attention as an approach to knowledge production, a specific focus on the participation of community members in decision-making or governance of CBR is sparse. To assist in understanding governance of CBR in Canada and the nature and extent of public participation, we conducted an interview-based qualitative study with 54 respondents. Arnstein’s (1969) theory of participation was used as the theoretical orientation. Respondents’ experiences showed their participation in governance was generally organised through four groups of factors that modified participation: pre-existing conditions, arrangements of governance, actions of academic actors, and actions of community actors. Although community members’ participation in governance was largely contingent on the arrangements, structures and actions controlled or formulated by academics, and despite their relatively limited access to and engagement with real decision-making power, in general community members’ participation was satisfactory to them. However, the highest level of participation that Arnstein envisaged was rarely attained. Awareness of theory and practice of participation in research decision-making can help research decision-makers put in place the conditions and means for realising democratic goals and knowledge co-production. 
 
 Keywords: governance, decision-making, community-based research, public participation, Arnstein
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How this classification was reachedexpand
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 | 0.061 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".