Improving Young Entrepreneurship Education and Knowledge Management in SMEs by Mentors
Bibliographic record
Abstract
Small and medium sized enterprises (SMEs) have a great importance in Europe, but the dynamic nature of markethas created a competitive incentive among companies requiring them to improve their critical area of knowledgemanagement (KM) and corresponding skills to create new business. More entrepreneurs, more innovation andgrowth are necessary and this could be realized particularly by supporting young people who would like to beentrepreneurs but the existing education and training programmes are insufficient.Mentoring is in comparison with coaching/counselling a special form of active supporting of entrepreneurshipcompetence, a “natural support” helping also young people with special needs to believe in themselves and boost herconfidence. The mentoring approach helps young people giving them practical entrepreneurial support and SMEs toimprove the transfer and use of strategic knowledge. Knowledge is the key for all organizations and the success ofmany of them depends on the effective deployment and continual enhancement of their knowledge base so as to beinnovative and to remain/become competitive. The paper first describes the areas of research, mentoring,entrepreneurship education and KM and then used methods and results. Examples for projects supporting theimprovement of young entrepreneurship education and KM also by using mentoring are given.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".