Bibliographic record
Abstract
A survey of the perspectives of port users in North America identified and evaluated a key, yet underresearched, component of port performance, that is, effectiveness in delivering port services to port users. This research responds to recent calls by port scholars for studies measuring port performance for more analytical emphasis on users' perspectives. The survey, conducted with the help of 13 professional associations in the United States and Canada, resulted in an understanding of (a) how port users evaluated the ports that they use, (b) what was most important to users in terms of the attributes of services, and (c) how users evaluated the performance of ports that they used on the U.S. East Coast and in Canada. Participants were asked to rate the importance of various performance criteria and then to apply them by evaluating the performance of the ports they used on those dimensions. To analyze the findings, the survey used a gap analysis and normalized pairwise estimations to measure the actual influence of a criterion on port performance. With performance being more than just satisfaction, this process generated knowledge on what contributed to better performance in the eyes of users in two different regions of North America. The results of this type of study will enable stakeholders to compare performance from specific ports as input to decision making and enable ports to focus their resources on improvements that matter to their customers and supply chain partners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".