Knowledge Mobilization in a College Context: Constructing Meaning in Applied Research Communication
Bibliographic record
Abstract
Knowledge mobilization (KMb) is the communication process by which researchers engage with stakeholders to produce, co-produce, and share knowledge. The question of how researchers can better mobilize knowledge at each stage of the research process has become a matter of acute public and academic interest. Yet existing research indicates that researchers are often reticent to engage with non-academic stakeholders; studies have found many do not have the necessary time, skills, or resources. Furthermore, most research on KMb has focused on large institutions or a university context, despite the fact that Canada’s colleges also produce a significant proportion of Canadian research. This study investigated college researchers’ understandings of and approaches to KMb using in-depth, semi-structured interviews. Eleven participants representing six Canadian community colleges and polytechnic institutes were interviewed about their approaches to KMb and the institutional or systemic factors that influence how they perceive KMb and carry out KMb activities. Participants defined KMb as a complex, reciprocal process with the potential to elevate their field, solve problems, and inform important decisions. Key KMb facilitators identified by the participants included low professional pressure to publish academically, which freed up time and resources for non-traditional approaches to KMb; funding structures that incentivize effective and ongoing KMb; and strong collaborations with other college departments, especially communications and marketing. Barriers included challenges to academic freedom, long delays caused by institutional and legal oversight of KMb, and certain gaps in funding opportunities.
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.
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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.024 | 0.095 |
| Scholarly communication | 0.034 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| 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".