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 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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".