Entrepreneurial Logistics Education in Institutions of Higher Learning: The Case of Universiti Malaysia Kelantan
Bibliographic record
Abstract
In recent times, the unemployment rate has been reported increasing as year passes as well as the nation’s economy been fluctuating (Department of Statistics Malaysia, 2016). Besides that, from the survey, it has been noted that students that eager to step into entrepreneurship field were insufficient to cope up with nation’s Gross Domestic Product (GDP). Therefore, the main objective of the research is to determine the number of logistics students possesses entrepreneurship personality. In addition, to identify whether the program offered could shift the mindset of logistics students become an entrepreneur as well as to provide suggestion to UMK in order to sustain the logistics program, survey was done in Universiti Malaysia Kelantan (UMK) City Campus, where 123 respondents were chosen out of 180 fourth year logistics students referring to Krejcie and Morgan table. Convenience sampling has been used to determine the sample size. The data was collected using a quantitative method where the questionnaires were designed using Holland Test Occupational Theme Model. Descriptive analyses were conducted to analyze the data obtained. From the finding, it’s proven that majority of logistics students are interested in becoming an entrepreneur as well as the entrepreneurial education offered by UMK is fitted in instilling plus producing more entrepreneur undergraduates especially in Logistics and Distributive Trade program.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".