Reflections on creative and collaborative teamwork in charrettes, 24 hours of innovation
Bibliographic record
Abstract
T he following article outlines three discussion topics regarding creative teamwork in charrettes 1 of technological projects: a) team building and their required production time, b) an analysis of the issues presented and the teams response, as well as, c) the use of time in work sessions. This empirical study was carried out within the context of the 24 Hours of Innovation international competition, with the participation of university students from: Ecole de Technologie Superieure de Montreal (ETS, Canada), l'Ecole Superieure des Technologies Industrielles Avancees (ESTIA, France), l'Universite de Technologies Compiegne (UTC, France), l'Universite de Technologie de Belfort-Montbeliard (UTBM, France) et l'Universite Ziguinchor (Senegal), Ecole de Design Industriel de l'Universite de Montreal, HEC-Montreal, and in partnership with, Specialty Vehicles and Transportation Equipment Manufacturers' Association (AMETVS).
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 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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".