Transparency in port governance: setting a research agenda
Bibliographic record
Abstract
Abstract This study examines the concept of transparency as practiced (or not) in ports. It explores the availability of information to the general public and port stakeholders through the ports’ most public face—its website, studying public ports in North America, Europe, and Latin America and the Caribbean. This exploratory research centred on identifying the parameters that would be useful for the general public to have sufficient information to monitor, review and in many cases, participate in the decision-making processes carried out by the port authority, irrespective of whether or not laws mandate such disclosure. Fifty-one items were identified for the examination of each port’s website, focusing primarily on four major categories: decision-making governance, port communications and accessibility, transparency in reporting and in port operational activities. Using nine items as proxies for the 51, the research reveals uneven levels of port transparency both regionally and by governance model. The study reveals a need for increasing and differentiating the existing levels and standards of transparency in the governance of the port industry, and for greater consistency between ports within and across regions. The study concludes with a research agenda for future research.
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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 | 0.045 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.027 | 0.040 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".