An investigation on the professionalization of education in Maritime logistics and supply chains
Bibliographic record
Abstract
Purpose This paper aims to investigate the development of logistics and supply chain education through conducting comparative study between high diploma and associate degree. This study will critically review the added value of sub-degree courses of professional education. What exactly drives sub-degree students to enroll for a high diploma and associate degree program in maritime logistics and supply chain studies? How do they select to enroll such programs? Do such programs foster the students to equip in the professions? What do they look for obtaining professional status afterwards? Design/methodology/approach To address the stated queries, this study will analyze students’ evaluation of the effectiveness of sub-degree education and their motivation on enrolling these courses through a questionnaire survey. Findings In the context of higher education, sub-degrees of professional studies experienced tremendous growth in recent decades. Many academic institutions have recorded an upward trend in providing professional education on subjects that traditionally focused on apprentice-style, non-academic learning approach. However, the reasons behind the steady growth of the demand of sub-degree level of professional education have been under-researched. Research limitations/implications This research is based on Hong Kong data only. Originality/value The paper not only increases the scope and depth of research area in logistics and supply chain education but also contributes theoretically to the understanding on the curriculum of sub-degree logistics and supply chain programs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".