When Artists and Designers Inspire Collective Intelligence Practices: Two Case Studies of Collaboration, Interdisciplinarity, and Innovation Projects
Bibliographic record
Abstract
Current mainstream collaborative processes and practices are not always fit to deal with the complexity of our society and the problems it generates. The lack of complexity-based practices for empowering collective intelligence conditions makes it difficult to address and solve intertwined multi stakeholders situations. As a disciplinary attitude can rarely succeed to solve complex and wicked problems, there is relevance and a need to question today’s mainstream approaches to collaboration and innovation. We explore this issue by asking how design can be of help to lead this reflection and to translate collaboration into pragmatic activities. We propose that by focusing on a constructivist paradigm and an interdisciplinary approach, collective intelligence can be constructed. It will then generate new ways to address complex situations. To support this, we draw from two interdisciplinary projects done in two organizations where collaborative design has translated into various social practices. In one case the creative process involves artists and managers, in the other, collaborative reflective practice within an HCI project brings stakeholders to focus on a human-centered approach to design and sustainability. We examine how design has in each case been of help, and finally, we conclude by presenting pragmatic ideas easily translatable into guidelines for fostering collective intelligence.
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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 | 0.057 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.045 | 0.035 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".