‘Community control’ in CBPR: Challenges experienced and questions raised from the Trans PULSE project
Bibliographic record
Abstract
Newer forms of community-based participatory research (CBPR) prioritize community control over community engagement, and articles that outline some of the challenges inherent in this approach to CBPR are imperative in terms of advancing knowledge and practice. This article outlines the community control strategy utilized by Trans PULSE, an Ontario-wide research initiative devoted to understanding the ways in which social exclusion, cisnormativity (the belief that transgender (trans) identities or bodies are less authentic or ‘normal’), and transphobia shape the provision of services and affect health outcomes for trans people in Ontario, Canada. While we have been successful in building and supporting a solid model of community control in research, challenges have emerged related to: power differentials between community and academic partners, unintentional disempowerment of community members through the research process, the impact of community-level trauma on team dynamics, and differing visions about the importance and place of anti-racism work. Challenges are detailed as ‘lessons learned’ and a series of key questions for CBPR teams to consider are offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.146 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.040 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.009 | 0.010 |
| 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".