Questioning the Meaningfulness of Rigour in Community-Based Research: Navigating a Dilemma
Bibliographic record
Abstract
As community-based research (CBR) continues to emerge, CBR practitioners are beginning to ask, “How do we know if we are doing CBR well?” For some, this question may bring to mind the concept of rigour. Yet, how meaningful is rigour among diverse CBR partners from community, government, and academia? Using an exploratory approach, we engaged in dialogue a group of seven CBR practitioners from diverse contexts and asked the question, “Is rigour a meaningful concept in CBR?” We used interpretive description to analyse the interview and guide the application of findings in CBR practice. The findings are presented as three themes: Obligation, Representation, and Turn to Action. Participants expressed a sense of obligation to meet often competing expectations to do CBR well. The fulfillment of one obligation sometimes meant compromising another, thus presenting a dilemma to CBR practitioners. Representation outlines participants' beliefs that some obligations can be met through the analysis, interpretation, and carefully contextualized presentation of research findings on behalf of CBR partnerships. In Turn to Action, participants described their desire to participate in the co-construction of understanding and identified a need to conceptualize the meaning of doing CBR well. We recommend that practitioners of CBR continue to form communities of practice in which to engage in dialogue about rigour; together, we can navigate the identified dilemma and collaboratively construct what it means to do CBR well. Specifically, we recommend that communities of CBR practice strive to: (a) be transparent during CBR collaboration, (b) be in dialogue with other CBR practitioners, and (c) co-construct the meaning of doing CBR well.
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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 | 0.094 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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".