Harnessing social media and cloud-computing technologies for co-design in open collaborative innovation: the case of 24 hours of innovation
Bibliographic record
Abstract
Designers and industry both agree that new uses of media and cloud computing technologies have had a major impact on the way designers receive and share information and knowledge. Our research team was interested in examining whether these technologies also directly affect the social dynamics in co-design meetings. In this paper, we describe the dynamics observed during an annual international competition, 24 Hours of Innovation , and at two co-design sessions held at the Ecole de technologie superieure s INGO Innovation Center in Montreal. Our aim was to develop a Knowledge Management System that supports the co-design experiences present in an open collaborative innovation process. We analyzed the use of media by participants during four periods of the event: announcement, information, contributions, and polling of projects. We followed 135 teams from more than 20 universities, from 5 continents, which participated in the 5th edition of 24 hours of Innovation in Montreal. This competition also included 14 remote teams that participated in the 6th edition at ESTIA France.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".