Exploring practices in collaborative innovation: Unpacking dynamics, relations, and enactment in in‐between spaces
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.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".