Community-Academic Peer Review: Prospects for Strengthening Community-Campus Engagement and Enriching Scholarship
Bibliographic record
Abstract
Scholarly peer review is hailed as an indispensable process to maintain quality and rigour in research publications. However, there is growing recognition of the limitations of peer review and concerns about the unexamined assumptions surrounding the processes that favour academic ways of knowing. In this paper, we build on these debates by exploring the possibilities for engaging communities in shaping and assessing the value of knowledge. Drawing on insights of a community-academic peer review pilot project through a pan-Canadian research partnership, we reflect on the value of incorporating community perspectives into research review processes and challenges of scaling-up these efforts. We argue that the perspectives of community-based practitioners are a necessary part of peer review—especially for Community-Based Research—to increase validity and accountability. This process gives academics and practitioners the power to collectively assess and evaluate knowledge products. Fundamentally, these efforts are about reviving higher education and critical research as part of a democratic public sphere that is open, inclusive, and relevant. We conclude by reflecting on the value of incorporating community perspectives into the peer review process. We also offer recommendations on how to recognize and incorporate community knowledge and experiences into assessment structures.
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 | 0.402 | 0.490 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.021 | 0.029 |
| Scholarly communication | 0.046 | 0.043 |
| Open science | 0.010 | 0.044 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".