Exploring Community-based Research Values and Principles: Lessons Learned from a Delphi Study
Bibliographic record
Abstract
Community-based research (CBR) is a relatively new methodology characterised by the co-generation of knowledge. As CBR is integrated into institutional frameworks, it becomes increasingly important to understand what differentiates CBR from other research. To date, there has been no systematic study of CBR values and principles, which tend to be offered as a list of considerations that are taken as given rather than problematised. Similarly, research has not explored the ways in which understandings of CBR's underlying values differ among individual researchers compared to the broader research values of a large university. In this article, we report the findings of a Delphi study which addresses these gaps through a systematic, cross-disciplinary survey of CBR researchers at a large Canadian research university. Our findings indicate diverse and complex understandings of both the potentially political nature of CBR and the perceived values of the respondents' institution.
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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 | 0.173 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".