Challenging knowledge divides: Communicating and co-creating expertise in integrated knowledge translation
Bibliographic record
Abstract
To solve complex problems, it makes sense to seek diverse perspectives to develop research-based solutions. In the Canadian health sector, this collaborative approach to research is often called integrated knowledge translation (IKT). This thesis is concerned with how boundaries are both essential and obstructive in IKT. While the goal of partnering is to leverage different expertise, diversity also presents some of the most significant challenges to success, creating barriers that block communication and constrain knowledge sharing. Using situational analysis to explore interview and case study data, I explore how knowledge boundaries are experienced within IKT projects. I outline four discursive positions that emerge, and argue that recognizing their distinct characteristics is important for progress in IKT. I also compare and contrast concepts of boundary work and boundary objects as theoretical lenses for IKT analyses, and argue that broadening our conceptual toolbox is beneficial for the study and practice of IKT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".