It is time to acknowledge and act on the importance of power in integrated knowledge translation
Bibliographic record
Abstract
Integrated knowledge translation (IKT) has emerged as an approach to research centered on collaboration between researchers and knowledge users, particularly in health research. There has been a growing focus on power within the IKT literature, especially the concern that overlooking power inequities within IKT partnerships may reproduce forms of knowledge production and dissemination that do not align with IKT's aspirations of shared decision making to produce useful and usable research findings. However, there remain significant gaps in our understanding of how to address and attend to power in IKT. The lack of conceptual precision around power complicates these efforts. In this commentary, we draw on existing literatures that have grappled with the notion of power to sensitize those who study and engage in IKT to the importance of power and to identify helpful ways of thinking about power in IKT. We propose that it is time to not only acknowledge the importance of power in IKT but also to develop empirically based strategies through which the many dimensions of power can be identified and navigated.
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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 | 0.223 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.147 |
| Scholarly communication | 0.046 | 0.074 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.042 | 0.082 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".