Studying Irregular Migration through Crime Science: Insights into Smuggling and Trafficking on the Central Mediterranean Route to Europe
Bibliographic record
Abstract
In this thesis, I investigate irregular migration, smuggling, and related harms involving exploitation and detention on the Central Mediterranean Route, using a crime science approach. The thesis is divided into three main analytical components. First, I deconstruct 71 migration journeys into a directed weighted graph based on interviews with migrants arriving in Malta from Libya. Through scripting, 81 typical activities associated with migration were identified. Each journey was unique, and many were complex and long, involving different sequences of activities and lasting on average 18 months. Two thirds of participants worked during their journeys, another two thirds were detained before Malta, and a quarter were subjected to forced labour. \n \nSecond, I analyse the graphed migration system by developing algorithms to identify repeated cycles of activities across participants. Of the 81 activities identified, 70% were repeated by the same participants. For example, the activity ‘wait in detention’ was repeated on 18 journeys, on average three times. A total of 174 distinct cycles of activities were found, 22 of which were shared across several participants, including activity sequences linking anti-smuggling efforts with detention and forced labour. Significantly, identified patterns highlight how migrants can get stuck in cycles of im/mobility on their journeys. \n \nThird, I apply a situational lens to analyse harms linked to detention and forced labour. The results demonstrate new transitions and blurred categories between smuggling and trafficking. Sometimes, the presence of agency in trafficking journeys contrasted with its absence in smuggling journeys. \n \nMy findings underscore the urgent need for a reassessment of border control policies and detention practices, emphasising humane treatment, independent oversight, and adherence to international law. Addressing the systemic issues of repeated cycles of harm requires a multi-faceted approach that prioritises protection and support for vulnerable populations, while also considering the broader ethical implications and potential unintended consequences of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".