The paradox of diversity's influence on the creative teams Lessons learned from the analysis of 14 editions of "The 24h of innovation" hackathon
Bibliographic record
Abstract
The main objective of this paper is to understand the mindset and characteristics of a team in order to foster creative and innovative thinking. Especially, we want to analyse the influence of the diversity of scholar's affiliation within student's team on their performance during early design phase. For that purpose, we focus on one innovative event to foster creativity and design thinking of students during 24 hours design phase. This event is 'The 24h of innovation®" challenge which is an event created by the institute of technology ESTIA in Biarritz since 2007 (http://24h.estia.fr). During the period 2007-2019, we have organized 14 French editions and we collected the data of profile's participants in order to characterise the team's diversity. The paper demonstrates the paradox of the diversity that can increase sometimes positively the average team performance, but also the non-diversity can be a positive factor for the team excellence
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".