Policing Complex Criminality in and through Major Seaports
Bibliographic record
Abstract
This chapter provides a framework for analysing large-scale, organised and complex criminality through and within major seaports and the challenges of policing these activities. The chapter’s main aim is to analyse how organised crime, organisational crime and forms of collusion and/or corruption on the waterfront and in/through seaports should be understood as complex crimes, in as much as they involve different types of activities, more or less serious or harmful in nature and more or less local in reach. Drawing on data collected in the ports of Genova, New York/New Jersey, Montreal, Melbourne, Liverpool and Gioia Tauro in particular, the chapter reflects on practical challenges and the practices of policing the port space. In doing so, the chapter establishes that the policing of complex crimes on the waterfront and in/through seaports is a form of hybrid policing, between high and low control mechanisms. It concludes with an analysis of the consequences this has for local partnerships and long-lasting interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".