Bibliographic record
Abstract
The role of design, both expert and nonexpert, in the ongoing wave of social innovation toward sustainability. In a changing world everyone designs: each individual person and each collective subject, from enterprises to institutions, from communities to cities and regions, must define and enhance a life project. Sometimes these projects generate unprecedented solutions; sometimes they converge on common goals and realize larger transformations. As Ezio Manzini describes in this book, we are witnessing a wave of social innovations as these changes unfold—an expansive open co-design process in which new solutions are suggested and new meanings are created. Manzini distinguishes between diffuse design (performed by everybody) and expert design (performed by those who have been trained as designers) and describes how they interact. He maps what design experts can do to trigger and support meaningful social changes, focusing on emerging forms of collaboration. These range from community-supported agriculture in China to digital platforms for medical care in Canada; from interactive storytelling in India to collaborative housing in Milan. These cases illustrate how expert designers can support these collaborations—making their existence more probable, their practice easier, their diffusion and their convergence in larger projects more effective. Manzini draws the first comprehensive picture of design for social innovation: the most dynamic field of action for both expert and nonexpert designers in the coming decades.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".