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.
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.
How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".