What must a lecturer/instructor (teacher) be able to do to inspire entrepreneurship and business students?
Bibliographic record
Abstract
This paper discusses what a lecturer/instructor (teacher) must be able to do to inspire entrepreneurship and business students. Entrepreneurship, particularly start‐up business, has become a top priority in national government policies due to its ability to drive creativity, innovation, competitiveness,employment and growth. The goal is not to make the students rush to become entrepreneurs or business‐oriented professionals but rather provide them with tools that enable realistic self‐evaluations and learn to recognize different opportunities around them. The study seeks to define the skill sets that the inspiring entrepreneurship and business teachers consider essential to their work. Teachers expressed their views in small focus groups of their peers, i.e. other teachers. The theoretical framework consists of theories dealing with the general skill sets and expertise of the teachers. The empirical data were collected through a Finnish adaptation of the Canadian DACUM (Developing A CUrriculuM) model which is used to analyze the contents of the requirements of various occupations. A separate questionnaire has been used to support the data collection. This study indicates that most of the entrepreneurship teachers are females whose pedagogical experience seems to be more comprehensive and long lasting than their male colleagues possess. However, they are often short of practical skills. On the contrary, the male colleagues have more experience in practice but with quite narrow pedagogical skills. The female entrepreneurship teachers tend to be inspired, but they often have too little experience in practical working life. According totoday’s trend, entrepreneurship teachers are anticipated to be not only traditional teachers but also entrepreneurs with their own company.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".