Cocreating Rigorous and Relevant Knowledge
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
The communities of research and practice are embedded in different knowledge systems; research favors rigor, while practice favors relevance. Many management scholars have concluded that cocreating knowledge with these two knowledge systems is difficult and rare, with such criticisms or reservations often being based on an event-based account of cocreation in which the cocreation activities occur over a distinct period of time with a clear beginning and end. However, event-based accounts bring the challenges of cocreation into focus. In the present research, we have assumed a process ontology, which brings the dynamics into focus and recognizes that cocreation is continuous. We observed two projects in which researchers and managers collaborated to generate knowledge related to business sustainability, and conducted 67 interviews with 47 participants in similar projects. We found that, by making the process explicit, participants were better able to cocreate knowledge. Furthermore, we identified two devices that helped to make the process explicit: (1) making temporal connections between events and (2) recognizing the incompleteness of the objects. Our study contributes to prior research on cocreation by showing that cocreation occurs not just within events but also between events, so that rigor and relevance are imbricated over time.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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