Perceptions of Logistics Students on Internship Programme: The Case of Private Higher Institution in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the effects of knowledge, skills, and talent on logistics internship programme within the context of Malaysian private higher education institutions. Despite extensive research on voluminous literature on logistics internships, there is very little empirical research that has examined this scope of study. The proposed research framework and hypotheses were developed with the intention of examining this relationship. Hierarchical regression analysis was employed to explore the relationship between knowledge, skills, talent and logistics internship programme. The results revealed that talent was positively and significantly associated with logistics internship programme (β = .503, p < .001). The findings are considered to have made a significant contribution to the literature, by using the Malaysian logistics undergraduate students which enhances our understanding on the need to have a significant learning outcome for logistics internship programme. Key words : Logistics internship programme; Knowledge; Skills; Talent
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 it