Community Members as Facilitators: Reclaiming Community-Based Research as Inherently of the People
Bibliographic record
Abstract
This article aims to rethink the positionality of community in community-based research collaboration and advocate the need for community members to facilitate CBR processes to counter power imbalances in community-university engagement. I reflect on my lived experience as a community-based facilitator through a feminist post-structural lens focused on the interplay between concepts such as subjectivity, margin-centre and performativity. I argue that, despite the community-engaged scholarship egalitarian ideal, university-community engagement still echoes the old researcher-researched binary in which academics remain the hegemonic pole. In addition, as a medium of power/knowledge, the university fabricates the community and its marginality. Thus, a margin-centre relationship is established, in which community groups must claim their marginality to receive a share of the centre (the university), such as research skills and information. In these margin-centre dynamics, university and community can be understood as identities and subject positions to be taken up by individuals. In essence, these positions are expressions of regulatory power that normalises subjectivities, a condition in which individuals exist as subjects in the social space. Insights from the work of Judith Butler lead to the understanding that, in order to conceive community members as CBR facilitators, normalised and stabilised binary identities (university-community) should be unsettled. This entails individuals who are subjected as ‘the community’ to escape subjection by moving towards recognition of a subjectivity that is not prescribed or is still marginalised within the discourse. In escaping subjection, community groups may exercise power in order to establish new power relations in which CBR becomes more community-led, yet still collaborative.
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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 | 0.097 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.024 | 0.113 |
| Scholarly communication | 0.027 | 0.041 |
| Open science | 0.005 | 0.043 |
| Research integrity | 0.008 | 0.011 |
| 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".