Assessing port governance models: process and performance components
Bibliographic record
Abstract
This paper develops a conceptual framework that integrates various relevant port performance components in a way that can be used for a comprehensive port evaluation and adjustment of existing port governance models. The paper presents a synthesis of the literature on port governance models and port performance, arguing that the process of change is a dynamic one, and that the performance outcome of a reform process influences the next round of reforms. It also explores the potential for decomposing performance into two different, although related, port performances components, namely efficiency and effectiveness. Bringing into the analysis concepts like the need to integrate users’ satisfaction in port performance assessment, the paper explores the content of each of these components and their relationship. This discussion, along with empirical evidence provided by port authorities, leads to the conclusion that governance decisions, both at firm and government levels, are largely based on a very limited assessment of port performance. The effectiveness of port reform is largely neglected, with user perspectives not being an integral part of an effort to improve performance by the port or as feedback to assess the effectiveness of the governance model imposed by the government's port policy.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".