Bibliographic record
Abstract
The purpose of this paper is to empirically examine whether there are significant relations among rankings of cross-efficiency, web accessibility, and website evaluation. For this purpose, the study uses the KWAH-4 method developed by the Web Accessibility Laboratory in Korea, website evaluation method developed by the Business Development Bank of Canada (BDC), and the cross-efficiency model for 13 Asian container seaports including Korean, Chinese, and Japanese main ports in 3 years (2009, 2010, and 2013) using data for two cases: three inputs (depth, total area, and number of crane) and one output (TEU) in the first case and three inputs and two outputs (TEU and BDC overall score) in the second case. The main empirical results are as follows. First, the ranking orders of cross-efficiency, web accessibility, and website evaluation overall scores are not significantly correlated with each other. Second, if the BDC overall score is included in the output element, the correlation results are improved. However, the correlation coefficient is still low. The container port policy planners should introduce and consider the web accessibility and website evaluation scores when evaluating an efficiency-increasing plan for Korea’s main container ports.
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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".