Helping start-ups and public organisations to align: Co-producing the co-creation context in a public hospital
Bibliographic record
Abstract
Incubation facilitates innovation performance and knowledge production for large organisations, while allowing start-ups to connect with potential users who hold valuable information. However, aligning the goals of parent organisations and hosted start-ups in co-creation partnerships remains a challenge, particularly in the complex and institutional context of public organisations. To fill the research gap, this paper draws on the co-production theory of support to explore how a large public organisation co-produces an adaptive context for co-creation with start-ups. Based on a 12-month ethnography in a public university hospital centre, the unique characteristics and complexities of co-creation initiatives in public organisations are explored by focusing on the multi-stakeholder nature of the innovation process. Following a processual approach, we unpack the evolving dynamics of managing tensions through the co-creation process. The article contributes to the understanding of public incubators, compensatory practices in asymmetrical co-creation relationships, and the co-production of incubation support in public organisations.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".