From organizing for innovation to innovating for organization: how co-design fosters change in organizations
Bibliographic record
Abstract
Amongst the plethora of methods that have been developed over the years to involve users, suppliers, buyers or other stakeholders in the design of new objects, co-design has been advertised as a way to generate innovation in a more efficient and more inclusive manner. Yet, empirical evidence that demonstrates its innovativeness is still hard to come by. Moreover, the fact that co-design workshops are gatherings of participants with little design credentials and often no prior relationships raises serious doubts on its potential to generate novelty. In this paper1, we study the contextual elements of 21 workshops in order to better understand what codesign really yields in terms of design outputs and relational outcomes. Our data suggest that codesign emerges in crisis situations and that it is best used as a two-time intervention. We also suggest using collaborative design activities as a way to bring about change through innovation.
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How this classification was reachedexpand
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".