Categorizing and Fixing Variables on Entrepreneurial Intention through Qualitative Research
Bibliographic record
Abstract
Many policies and regulations have been made by Indonesian Government to enhance the quality of graduates in higher education. Numerous programs have been launched to build the mentality and business awareness of university students such as National Science Fair (PIMNAS), Student Entrepreneur Program (PMW), Student Creativity Program (PKM), Business Incubation Program and many other programs that can enhance the propensity of the students to start up a business. However, there are only 17% of the graduates who are willing to become entrepreneurs each year. This indicates that students have a lack of intentions to become entrepreneurs. However, there is less research and literature to support the argument that the students do not have entrepreneurial intention. In order to explore the entrepreneurial intention among the Indonesian students graduating universities and business schools from a qualitative study was conducted. The methodology used to develop an appropriate variable for entrepreneurial intention is focused group discussion (FGD), case analysis, interviews and Delphi technique measures the student's entrepreneurial intention. There are 20 experts were willing to take part in this study and the study identified 4 factors that eventually suit the student desire to deal with entrepreneurship. This study gives a valuable contribution to the higher education institutions, to orient the students to become entrepreneurs through right grooming by ensuring better entrepreneurship program as well as the curriculum.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".