Bibliographic record
Abstract
The idea for this book emerged from a workshop organized by the four authors of this book in May 2020. The workshop brought together 16 practitioners and nine researchers with expertise in the field of port policing, port security, organized crime and border control. Funded by the British Academy, the ‘Secur.Port’ workshop was organized by the University of Essex and Strategic Hub for Organised Crime Research (SHOC) at the Royal United Services Institute for Defence and Security Studies (RUSI), with the support of Ghent University and Research Foundation Flanders. The event featured contributors from seaports including Melbourne (Australia); Antwerp (Belgium); Rotterdam (The Netherlands); Genoa (Italy); Montreal (Canada); New York and New Jersey (US) and Liverpool (UK). Two EU-funded projects also contributed to the overall discussion, namely the PASSAnT project (for new port security technologies), led by the Belgian Innovation Network for Security, Iungos, the Belgian Vias Institute and the Dutch Institute of Technology, and the European Union (EU) funded project ‘Improving Port Security in Western and Central Africa’, led by Expertise France. Practitioners were asked to present on the two enforcement or security issues they deemed most relevant in their port of reference. A Q&A followed presentations and researchers acted as thematic discussants, contextualizing and analyzing the emerging themes from each session. These themes are presented in what follows as an informal introduction to the key material and debates covered in this book, which draws on projects the four authors have worked on separately and together in relation to crime, security and ports over the past five to ten years. In addition to the Secur.Port project, the authors wish to credit Research Foundation Flanders for funding the ‘Ports of Call’ research project, including workshops in Ghent (2019) and Brisbane (2019).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.353 | 0.199 |
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".