A transdisciplinary approach for generating synthetic but realistic domestic sex trafficking networks
A transdisciplinary approach for generating synthetic but realistic domestic sex trafficking networks
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一种跨学科方法,用于生成综合但现实的家庭性贩运网络
DOI:
10.1080/24725854.2023.2169418
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发表时间:
2023
影响因子:
2.6
通讯作者:
Martin, Lauren
中科院分区:
文献类型:
--
作者:
Kosmas, Daniel;Melander, Christina;Singerhouse, Emily;Sharkey, Thomas C.;Maass, Kayse Lee;Barrick, Kelle;Martin, Lauren
One of the major challenges associated with applying Operations Research (OR) models to disrupting human trafficking networks is the limited amount of reliable data sources readily available for public use, since operations are intentionally hidden to prevent detection, and data from known operations are often incomplete. To help address this data gap, we propose a network generator for domestic sex trafficking networks by integrating OR concepts and qualitative research. Multiple sources regarding sex trafficking in the upper Midwest of the United States have been triangulated to ensure that networks produced by the generator are realistic, including law enforcement case file analysis, interviews with domain experts, and a survivor-centered advisory group with first-hand knowledge of sex trafficking. The output models the relationships between traffickers, so-called “bottoms”, and victims. This generator allows operations researchers to access realistic sex trafficking network structures in a responsible manner that does not disclose identifiable details of the people involved. We demonstrate the use of output networks in exploring policy recommendations from max flow network interdiction with restructuring. To do so, we propose a novel conceptualization of flow as the ability of a trafficker to control their victims. Our results show the importance of understanding how sex traffickers react to disruptions, especially in terms of recruiting new victims.
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DOI:
--
发表时间:
2014
期刊:
影响因子:
--
作者:
Lauren Martin;R. Lotspeich
通讯作者:
R. Lotspeich
DOI:
--
发表时间:
2020
期刊:
影响因子:
--
作者:
J. Winterdyk;Jackie. Jones
通讯作者:
Jackie. Jones
影响因子:
--
作者:
J. Caulkins;M. Kammer;R. Konrad;K. Maass;Laurène Martin;Thomas C. Sharkey
通讯作者:
Thomas C. Sharkey
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
F. Walk
通讯作者:
F. Walk
影响因子:
2.6
作者:
Paolo Campana
通讯作者:
Paolo Campana