That was fun, now what?: Modelizing knowledge dynamics to explain co-design's shortcomings
Bibliographic record
Abstract
Co-design workshops seek solutions to complex, multi-stakeholder issues. These ephemeral encounters bring together designers and uninitiated individuals who embark in a facilitated process that mobilizes a range of simplified design tools and methods. Despite co-design's benefits in terms of representation and acceptability, these workshops also come with limitations and often fall short of their intended goals. Proceeding from stylized facts informed by both our experience and the literature, this study investigates why co-design struggles at maintaining engagement and fails to consistently deliver innovative output regardless of the number of participants involved. Namely, we employ a model-building strategy to illuminate the main knowledge dynamics during workshops and to highlight a constrained ‘reactive expansion’ mechanism that explains known co-design's shortcomings. Implications for workshop facilitation and planning are offered in closing. • Despite its popularity, co-design comes with many downsides and shortcomings. • The difficulty to sustain engagement and to yield innovative outputs stands out. • This model-based study highlights knowledge dynamics to explain these shortcomings. • It shows a closed knowledge system that limits expansion, learning, and innovation. • The model's conditions and facilitation implications are also presented.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".