An analysis of migratory patterns and social network chains, ties, and bonds in human trafficking: Australia, Britain, Canada, and the United States, 2006-2011
Bibliographic record
Abstract
Research on human trafficking to date reveals certain limitations.First, little empirical research exists that focuses specifically on analyzing occurrences of human trafficking incidents.Second, very few human trafficking studies break away from the traditional male/female dichotomy of offender and victim.Investigations of disaggregated data on persons and circumstances remain rare.As a consequence, who does what to whom, and how often, remains unclear.This is problematic because current trends in trafficking research affect the direction of legislation meant to combat trafficking, and inadequate research can lead to inadequate legislation.It is paramount to understand human trafficking actors not only by their gender, but also by their actions, pathways, and networks that determine their inclusion in this underground economy.My intention is not to irreverently stir the pot, but to illustrate that the grey area is far too large for continuing calls to curb trafficking without understanding the logistics and rationale behind the action.This dissertation uses six years (2006)(2007)(2008)(2009)(2010)(2011) of legal case files from Australia, Britain, Canada, and the United States in order to consider how human trafficking operates transnationally and regionally.Focusing on the influence of networks and social ties, chains, and bonds, this dissertation addresses the nuances of human trafficking insofar as relationships between victim and offender are cultivated and employed in order to actuate the trafficking act.Analyses are two-fold: first, descriptive statistics are gathered in order to create a focused analysis of the particularities of those captured by the scope of this study; and, second, social network analysis (SNA) is used to better understand power dynamics within human trafficking networks.The structure of human trafficking networks is addressed based on the gender of offender and the prosecutorial action is addressed with a comparison of influence in the network and the imposed sentence length.The purpose of these two flows of analyses is to address how or whether a trafficker's gender affects his or her power and influence within the trafficking networks, as well as to investigate how law enforcement and criminal justice systems are affectively addressing the human trafficking problem.Findings suggest that insofar as power and influence within a trafficking network are concerned, gender does not matter:there is little to no difference between network centrality scores of males and females.Additionally, findings suggest that traffickers and trafficked individuals more often than Fraser
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".