A systematic approach for evaluating port effectiveness
Bibliographic record
Abstract
The objective of this paper is to examine how users evaluate port effectiveness and identify those constructs relevant to that evaluation. The three user groups studied are carriers, cargo interests, and suppliers of services at the port. The study team developed an on-line survey instrument and delivered it to Canadian port users with the assistance of eight industry organizations. The findings of the research are based on the contributions of 57 decision makers with port usage experience, many of whom fit into more than one group of users. The study concludes that the evaluation criteria influencing users’ perceptions of satisfaction, competitiveness, and service delivery effectiveness are different, and so while the determinants of these constructs have considerable overlap, they are different constructs. This paper also illustrates how independent (or third-party) evaluation of port performance might be used by a port to strategically improve its service to users, and therefore have value from a port perspective in its strategic planning.
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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 | 0.172 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.042 | 0.021 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".