Knowledge mobilization: Stepping into interdependent and relational space using co-creation
Bibliographic record
Abstract
In recent years, there has been a growing interest in using co-creation approaches, with academics and partners working together to create research and interventions to achieve impact. Action research typically starts with the question ‘how can we improve this situation?’ and then co-creates knowledge with and not on or for people. This approach contrasts with conventional approaches in which academics create knowledge and then disseminate it to users via conferences, reports etc. The co-creative approach involves a shift in academics’ thinking and approaches. The success of co-creation depends on the academic shifting from being self-focussed and independent to being other-focussed and interdependent. In this paper, we outline the theoretical background that has informed our thinking and practices related to knowledge mobilization, and our novel relational approach. We illustrate our approach using two co-created projects, focused on enhancing early literacy and supporting mothers with substance use problems. We hope that this will help others consider when it may be appropriate to use a co-creative approach and how to engage in this co-creation process, including awareness of common barriers and benefits.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".