Writing peer-reviewed articles with diverse teams: considerations for novice scholars conducting community-engaged research
Bibliographic record
Abstract
Given the growth of interdisciplinary and community-engaged health promotion research, it has become increasingly common to conduct studies in diverse teams. While there is literature to guide collaborative research proposal development, data collection and analysis, little has been written about writing peer-reviewed publications collaboratively in teams. This gap is particularly important for junior researchers who lead articles involving diverse and community-engaged co-authors. The purpose of this article is to present a series of considerations to guide novice researchers in writing for peer-reviewed publication with diverse teams. The following considerations are addressed: justifying the value of peer-reviewed publication with non-academic partners; establishing co-author roles that respect expertise and interest; clarifying the message and audience; using the article outline as a form of engagement; knowledge translation within and beyond the academy; and multiple strategies for generating and reviewing drafts. Community-engaged research often involves collaboration with communities that have long suffered a history of colonial and extractive research practices. Authentic engagement of these partners can be supported through research practices, including manuscript development, that are transparent and that honour the voices of all team members. Ensuring meaningful participation and diverse perspectives is key to transforming research relationships and sharing new insights into seemingly intractable health problems.
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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 | 0.665 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.046 | 0.042 |
| Scholarly communication | 0.069 | 0.045 |
| Open science | 0.014 | 0.048 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 0.016 |
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".