Navigating boundaries in coproduced research: a situational analysis of researchers’ experiences within integrated knowledge translation projects
Bibliographic record
Abstract
Background: Research coproduction is advocated as an approach to produce more impactful evidence, by valuing a diversity of expertise and integrating knowledge users into research processes. Yet, extant literature finds that trying to bridge boundaries between different types of knowledge can also cause collaboration challenges and present barriers to success in coproduction. Aims and objectives: To study how researchers understand and manage knowledge boundaries in coproduced health research, or 'integrated knowledge translation' (IKT) as it is referred to in Canada. Methods: Data were collected from: 1) semi-structured interviews (n=20) with researchers leading different IKT projects across Canada; and 2) participant observation and document analysis for an in-depth case study of one IKT project. Data were combined and analysed using situational analysis, a modified grounded theory approach to visually map patterns of discourse along salient axes of controversy. Findings: We describe four key discursive positions participants take concerning knowledge boundaries in IKT: to recognise and handle, respect and clarify, blur and integrate, or challenge and embrace. These are plotted relative to two salient axes: the degree to which participants viewed boundaries as a problem, and the degree to which they believed boundaries should (or could) be challenged. Discussion and conclusion: The four discursive positions identified will help those doing coproduced research to critically reflect on their own position(s) regarding boundaries in collaborative research, and strategically discuss, select, or switch discourses as needed to support their goals.
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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 | 0.056 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| 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".