Exploring the field and practice of knowledge mobilization: identifying common approaches and priority competencies using Q-methodology
Bibliographic record
Abstract
With the growing interest to understand knowledge mobilization (KMb) and knowledge brokering in practice, this Major Research Paper investigates the viewpoints of knowledge mobilization experts, researchers, intermediaries, and practitioners regarding priority KMb activities, and the competencies and skills required for such tasks. This mixed methods study employed Q-Methodology, with data collected in two major phases. First, expert interviews were conducted with 20 KMb experts from Canada and the UK to develop the study’s concourse and subsequent q-statements. Second, 91 participants completed an online Q-survey, with a Q-sort task with 49 q-statements and an activity-rating task with 31 activities. Respondents also answered a range of open-ended questions pertaining to their KMb work, training, and perspectives. A crucial component of this research is the use of the Great Eight Competencies Framework, also known as the Universal Competencies Framework (UCF). Analysis identified four distinct approaches to KMb and puts forward a preliminary hierarchy of KMb competencies, according to the survey responses. The proposed hierarchy advances current understandings of KMb in demonstrating commonalities in competencies across various professions and fields. KMb practitioners and researchers are encouraged to respond and refine this initial list of priority competencies according to their workplace and/or research contexts.
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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 | 0.090 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".