Building equitable community-academic research collaborations: Learning together through tensions and contradictions
Bibliographic record
Abstract
This article explores the findings from a multi-method study of a community-university research alliance (Assets Coming Together for Youth) that brings together multidisciplinary academics, graduate student research assistants, community stakeholders and youth research interns. The project undertook evaluative and reflexive research to better understand how these different partnership group members experienced the collaborative process. The article draws on focus group discussions with the four stakeholder groups, in-depth interviews with youth research interns and an online partnership assessment survey of partnership group members. Data highlight people’s ambivalence toward the partnership process. Despite a shared desire to collaborate, it is difficult to maintain a process that mobilises the outcomes of collaboration for the mutual benefit of all stakeholders. In this article, we explore three key factors that shape people’s perspectives on the partnership process: historical and institutional relations; structures for communicating across difference; and opportunities for learning. A close examination of these three factors suggests that ongoing opportunities for communication about, and learning from, people’s ambivalence (that is, uncertainty or hesitation) supports a positive and productive partnership process. Keywords: Community-academic research, collaboration, process, equity, learning, youth
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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 | 0.116 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.019 | 0.046 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".