That was fun, now what?: Modelizing knowledge dynamics to explain co-design's shortcomings
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
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it