Exploring practices in collaborative innovation: Unpacking dynamics, relations, and enactment in in‐between spaces
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
In the field of innovation management, the study of collaborative innovation has focused primarily on the type of networks to support innovation, the modularity of the product's architecture required to engage actors in collaboration, the strategies for patenting and knowledge appropriation, and the public policies likely to stimulate collaborative innovation. But given that many efforts to collaborate collapse and fail to generate the desired innovative value, previous research needs to be complemented with perspectives on what individuals and collectives actually do when creating collaborative innovation as they engage in “in‐between spaces”, spaces between actors created by and simultaneously creating social interaction, to understand the practices that both form and constitute the collaboration. Through such studies, new knowledge can be created building on detailed insights about what ensues as different actors engage in interaction to innovate together and contribute to identifying levers to build collaborative spaces that indeed foster innovation. With this special section, we wish to encourage innovation management scholars to rethink their approach to collaborative innovation research by complementing macro‐level insights with an exploration of the micro‐foundations of collaborative innovation to gain a more nuanced understanding of collaborative dynamics, relations and enactment.
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 | 0.003 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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