Technical and Scale Efficiency Analysis of 25 Nort Mediterranean Ports: A Data Envelopment Analysis Approach
Bibliographic record
Abstract
The strategic position of the northern Mediterranean ports gained importance with the announcement of Chinese investments, which form part of the One Belt One Road project. The research presented in this paper focuses on small ports, which have not heretofore been the subject of interest. In the first half of 2019, both Croatia and Italy agreed on participation in the project, which aims to shorten the journey from China to central Europe by changing sea route destinations to ports in the Adriatic Sea. This paper examines the technical and scale efficiency of 25 ports in Croatia, Italy and Slovenia, as a possible prerequisite for investments. The research uses Data Envelopment Analysis (DEA) variable returns to scale an output-oriented model on a panel data sheet, for 25 ports in the period from 2009 to 2018. This research suggests that the number of efficient ports, in this case, is not directly related to the size of the port or to the country in which it is located. However, it is more often the case that larger ports are more efficient. For all inefficient ports, the DEA provides best practice examples to which ports should aspire and therefore highlights the practical implication of the work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".