Exploring the Determinants of Crime-Terror Cooperation using Machine Learning
Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crim...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Semmelbeck, Julia [verfasserIn] Besaw, Clayton [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of quantitative criminology - Getzville, NY : HeinOnline, 1985, 36(2019), 3 vom: 19. Juli, Seite 527-558 |
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| Übergeordnetes Werk: |
volume:36 ; year:2019 ; number:3 ; day:19 ; month:07 ; pages:527-558 |
| Links: |
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| DOI / URN: |
10.1007/s10940-019-09421-0 |
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| Katalog-ID: |
SPR041026853 |
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| 520 | |a Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. | ||
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| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Besaw, Clayton |e verfasserin |4 aut | |
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10.1007/s10940-019-09421-0 doi (DE-627)SPR041026853 (SPR)s10940-019-09421-0-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Semmelbeck, Julia verfasserin aut Exploring the Determinants of Crime-Terror Cooperation using Machine Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Besaw, Clayton verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 36(2019), 3 vom: 19. Juli, Seite 527-558 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:36 year:2019 number:3 day:19 month:07 pages:527-558 https://dx.doi.org/10.1007/s10940-019-09421-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 36 2019 3 19 07 527-558 |
| spelling |
10.1007/s10940-019-09421-0 doi (DE-627)SPR041026853 (SPR)s10940-019-09421-0-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Semmelbeck, Julia verfasserin aut Exploring the Determinants of Crime-Terror Cooperation using Machine Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Besaw, Clayton verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 36(2019), 3 vom: 19. Juli, Seite 527-558 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:36 year:2019 number:3 day:19 month:07 pages:527-558 https://dx.doi.org/10.1007/s10940-019-09421-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 36 2019 3 19 07 527-558 |
| allfields_unstemmed |
10.1007/s10940-019-09421-0 doi (DE-627)SPR041026853 (SPR)s10940-019-09421-0-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Semmelbeck, Julia verfasserin aut Exploring the Determinants of Crime-Terror Cooperation using Machine Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Besaw, Clayton verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 36(2019), 3 vom: 19. Juli, Seite 527-558 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:36 year:2019 number:3 day:19 month:07 pages:527-558 https://dx.doi.org/10.1007/s10940-019-09421-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 36 2019 3 19 07 527-558 |
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10.1007/s10940-019-09421-0 doi (DE-627)SPR041026853 (SPR)s10940-019-09421-0-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Semmelbeck, Julia verfasserin aut Exploring the Determinants of Crime-Terror Cooperation using Machine Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Besaw, Clayton verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 36(2019), 3 vom: 19. Juli, Seite 527-558 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:36 year:2019 number:3 day:19 month:07 pages:527-558 https://dx.doi.org/10.1007/s10940-019-09421-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 36 2019 3 19 07 527-558 |
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10.1007/s10940-019-09421-0 doi (DE-627)SPR041026853 (SPR)s10940-019-09421-0-e DE-627 ger DE-627 rakwb eng 340 ASE 86.00 bkl Semmelbeck, Julia verfasserin aut Exploring the Determinants of Crime-Terror Cooperation using Machine Learning 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Besaw, Clayton verfasserin aut Enthalten in Journal of quantitative criminology Getzville, NY : HeinOnline, 1985 36(2019), 3 vom: 19. Juli, Seite 527-558 (DE-627)320578003 (DE-600)2017241-2 1573-7799 nnns volume:36 year:2019 number:3 day:19 month:07 pages:527-558 https://dx.doi.org/10.1007/s10940-019-09421-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 86.00 ASE AR 36 2019 3 19 07 527-558 |
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Semmelbeck, Julia |
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Semmelbeck, Julia ddc 340 bkl 86.00 misc Terrorism misc Organized crime misc Machine learning Exploring the Determinants of Crime-Terror Cooperation using Machine Learning |
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340 ASE 86.00 bkl Exploring the Determinants of Crime-Terror Cooperation using Machine Learning Terrorism (dpeaa)DE-He213 Organized crime (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 |
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ddc 340 bkl 86.00 misc Terrorism misc Organized crime misc Machine learning |
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ddc 340 bkl 86.00 misc Terrorism misc Organized crime misc Machine learning |
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Exploring the Determinants of Crime-Terror Cooperation using Machine Learning |
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Journal of quantitative criminology |
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Semmelbeck, Julia |
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exploring the determinants of crime-terror cooperation using machine learning |
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Exploring the Determinants of Crime-Terror Cooperation using Machine Learning |
| abstract |
Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. |
| abstractGer |
Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. |
| abstract_unstemmed |
Objectives This study seeks to further strengthen extant knowledge regarding terrorist group involvement in organized criminal activity through two means. First, it measures a set of environmental and organizational characteristics for a sample of well-known terrorist organizations based on the crime-terror literature. Second, it illustrates the utility of inductive research designs for examining patterns in the criminal behavior of terrorist groups for theory building and the potential risk classification of new terrorist organizations in the future. Methods The authors utilize a random forest classification algorithm to examine three sources of information about a broad set of environmental and organizational factors determined to be of potential importance in predicting when a terrorist organization will engaged in organized criminal behavior. First, it examines out-of-sample accuracy through bootstrap cross-validation estimation. Second, it quantifies the predictive efficacy/importance of each measured factor. Finally, it utilizes partial dependence functions to examine the relational trend between the most important predictive factors and variation in the presence of organized criminal behavior. Results The study finds three results. First, predictive accuracy using readily quantifiable factors about the criminal behavior of terrorist organizations is good but could be improved upon. Second, organizational factors such as group size, ideology and attack behavior out perform environmental factors in terms of predictive performance. Third, it finds that the most important predictor variables have a predominately non-linear relationship with whether the algorithm would classify a group as engaging in organized criminal behavior or not. Conclusions The study finds that theory building should seek to examine temporal variation in the organizational structure of terrorist groups as a fruitful way forward for further understanding when a group is likely to engage in organized criminal behavior. It also suggests that scholars should seek to engage more critically with concepts surrounding the potential non-linear pathways in which groups end up engaging in organized crime. Finally, the results illustrate the utility of modern machine learning algorithms and inductive research processes for both academic and practitioner needs alike. Especially when dealing with a complex phenomenon with imperfect data. |
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3 |
| title_short |
Exploring the Determinants of Crime-Terror Cooperation using Machine Learning |
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https://dx.doi.org/10.1007/s10940-019-09421-0 |
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Besaw, Clayton |
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10.1007/s10940-019-09421-0 |
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2024-07-03T19:46:46.790Z |
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| score |
7.3980246 |