Maritime Transport Security: Issues, Challenges and National Policies
Bibliographic record
Abstract
Contents: 1. Introduction Khalid Bichou, Joseph Szyliowicz and Luca Zamparini PART I: THEMES AND FRAMEWORKS 2. Maritime Security: Issues and Challenges Joseph Szyliowicz and Luca Zamparini 3. Overview of Contemporary Supply Chain Security Initiatives Khalid Bichou and Risto Talas 4. Economic Issues in Maritime Security Luca Zamparini 5. Risks and Costs of Maritime Security: Review and Critical Analysis Khalid Bichou 6. Maritime Terrorist Attacks against Seaports 1968 - 2007 Risto Talas and David Menachof 7. Maritime Piracy Analysis George Kiourktsoglou and Alec D Coutroubis PART II: POLICY APPLICATIONS 8. U.S. Maritime Security Policy: Achievements and Challenges Joseph Szyliowicz 9. Maritime Security in Canada Mary Brooks 10. Marine and Cargo Security Failures: European Cases Mark Rowbotham 11. Maritime Security in Nigeria Frank Ojadi 12. Maritime Security in Oman Sigurd Neubauer 13. Container Security at Indian Dry Ports Girish Gujar 14. Security in the Straits of Malacca in Recent Years Nazery Khaled 15. Maritime Security Regulations and Policies in Hong Kong: A Critical Review and the Development of a Risk Based Security Assessment Model Adolf Ng and Zaili Yang 16. Maritime Security and Piracy in Mauritius Shakeel Mohd 17. Conclusions (Comparative Analysis of Transport Policies and Relevant Benchmarks and Best Practices) Khalid Bichou, Joseph Szyliowicz and Luca Zamparini References Index
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".