Policing Complex Criminality in and through Major Seaports
Bibliographic record
Abstract
Introduction Ports are multivalent, liminal spaces. On the one hand, they are gates into the city, and on the other, they provide an exit door to the sea. Moreover, we can argue that they are multivalent spaces in as much as their nature is hybrid. As shown in the previous chapter, their activities constantly swing between the global and the local; global trade meets local workforce; global maritime industry meets local regulations. The multivalent and hybrid nature of the ports’ territory and purpose is crucial to understanding why policing the waterfront is such a complex task. This chapter provides a framework for analyzing large-scale, organized and complex criminality through and within major seaports and the challenges of policing these activities. It analyzes organized crime, organizational crime and forms of collusion and/or corruption on the waterfront and in/ through seaports by focussing on the challenges of countering them from a policing perspective. Drawing from data collected for completed research projects, especially in the ports of Genoa, New York/ New Jersey, Montreal, Melbourne, Liverpool and Gioia Tauro, this chapter reflects on practical challenges and the practices of policing the port space. In doing so, we start from two main standpoints and arguments. First, crime on the waterfront and in/ through seaports manifest 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. The chapter will address forms and manifestations of organized crime, including organizational crimes and corruption, as complex criminality. Accordingly, the chapter focusses on crime over harm, which is certainly relevant in this context (Bisschop, 2015), but not necessarily addressed in this chapter. Second, the policing of these complex crimes emerges as a form of hybrid policing, between high and low control mechanisms. This chapter therefore looks at the challenges of policing complex crimes through the lenses of high policing or hybrid policing approaches and the consequences this has for local partnerships and long-lasting interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.062 | 0.000 |
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 teacher head, 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".