Exploring the field and practice of knowledge mobilization: identifying common approaches and priority competencies using Q-methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it