Facilitating Elements for the Transmission of the Entrepreneurial Spirit in the Classroom
Bibliographic record
Abstract
There have been countless tests made to confirm that creativity is a critical skill for entrepreneurs and their entrepreneurial training (Schmidt, J. et al. 2012), even more, practices on divergent thinking increases the entrepreneurial skills of students to generate a greater number and range of ideas, but not, their approaches to solve problems in a creative way. This situation raises a number of questions that should be analyzed before adding creativity and techniques to improve the training of creative thinking to studies (classes), about and in entrepreneurship, as a training channel, improvement or even to the generation of entrepreneurs. Our goal is to show that entrepreneurship is a facet of creativity and that the "hidden" entrepreneur in the learner, will need to observe and confirm the existence of sufficient external constraints -context and social network- to release its entrepreneurial attitude, furthermore it will be essential, that teachers adopt an innovative approach that enables a suitable context, in this way almost intuitively, and vocational - show student behaviors associated with entrepreneurial attitudes. In our work, we found out that the implementation of an innovative teaching contributes to facilitate the transmission of the entrepreneurial spirit and improves the use of gen-preneur in class.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".