Bibliographic record
Abstract
According to a survey made by IBM in 2010, more than 1,500 CEOs from 60 countries and 33 industries worldwide identify “creativity” as the most important leadership competency for the successful enterprise of the future. The fact that this survey was done after the last economic downturn indicates us an important shift in attitude. Today's students are the future leaders of tomorrow. During their learning in school, they develop several leadership competencies including creativity. This study examines how students create in teamwork to find solutions to today's and tomorrow's problems. Since 2007, a creative contest named “The 24 hours of innovation” gathered around 3,500 students from 70 schools and universities of 40 countries. They were asked to find a creative solution in 24 hours to different technological problems submitted. We have analyzed the creative process of around 1,500 participants during those contests held from 2007 till now. In 2012, we have invited high school students to participate. Results show some similar processes used to create for the universities students but different creative patterns developed by high school students. Those different approaches could be the “creativity 2.0, student style” that could emerge from our future industry leaders.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".