From Research to Action: Four Theories and Their Implications for Knowledge Mobilization
Bibliographic record
Abstract
Integral to both knowledge mobilization and action research is the idea that research can and should ignite change or action. Change or action may occur at multiple levels and scales, in direct and predictable ways and in indirect and highly unpredictable ways. To better understand the relationship between research and action or change, we delineate four conceptualizations that appear in the literature. Reflecting on our experiences as collaborators in a community–university action research project that set out to tackle a “wicked” social problem, we consider the implications of these conceptualizations for the project’s knowledge mobilization plans and activities. The major lessons point to the importance of building capacity by nurturing collaborative learning spaces, of drawing many others – situated differently and with varied perspectives – into dialogue, and of embracing change within the project itself.
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 | 0.049 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.010 | 0.127 |
| Scholarly communication | 0.031 | 0.041 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".