Notice bibliographique
Résumé
This dissertation addresses the question of what factors and processes explain pathways of individuals with extreme beliefs towards different outcomes, including violent actions (e.g., murder), nonviolent actions (e.g., recruitment), or alternative outcomes (e.g., joining a terrorist organization abroad). The research is based on terrorist suspects in the Netherlands and utilizes a mixed-methods approach. The theoretical background of the research is formed by insights from social control theory, life course criminology and terrorism literature, examining risk and protective factors, triggers (events that accelerate or stop radicalization processes), the concept of “redemption,” and the relationship between terrorism and a criminal past. The aim is to generate more knowledge about terrorist suspects in general and differences between subgroups by using comparison groups. The method for the first section involves analyzing data from the Public Prosecution Service combined with data from Statistics Netherlands and judicial data. The first sub-study quantitatively compares terrorist suspects with regular suspects and siblings, examining various life-course factors one year before the terrorist crime. The next sub-study distinguishes between violent and nonviolent terrorist suspects and addresses demographic characteristics, household composition, socioeconomic status (SES) and criminal background. The second section analyzes probation files from the Dutch Probation Service. Possible signs and triggers leading to the terrorist suspicion, different life domains (socio-demographic characteristics, social network, health, and personality-related variables), criminal history, and online activities are mapped using descriptive statistics and qualitative interpretation. This section first compares violent to nonviolent terrorist suspects, and concludes with a chapter on foreign fighters. Findings in the first sub-study indicate that terrorist suspects share more similarities with other suspects than with their siblings, emphasizing the importance of studying protective factors. The next sub-studies highlight that there are more similarities than differences between violent and nonviolent terrorist suspects, however, differences were identified regarding social bonds, online activities, mental health, and SES. The last sub-study focuses on foreign fighters, noting the role of radical social contacts and the online environment in becoming a foreign fighter. The general discussion emphasizes the need for attention to lacking social bonds (e.g., being less often married), also in relation to subgroups. Furthermore, radical contacts, online activities, socioeconomic factors, and criminal history are discussed. Radical contacts and online activities related to the terrorist suspicion play an important role in the trajectories of terrorist suspects, especially for violent terrorist suspects and foreign fighters. In terms of SES, terrorist suspects in general (compared to their most akin sibling), and violent terrorist suspects more than nonviolent terrorist suspects, regularly have a relatively low SES. The findings also paint a broader picture that nonviolent and violent terrorist suspects may cope with (internal) hardship or profound events in different ways. Lastly, a substantial part of the terrorist suspects had already been suspected of crime prior to their terrorist suspicion. In contrast to similar comparison studies, which found that having a criminal past was associated with a violent outcome, this dissertation found no striking differences between violent and nonviolent terrorist suspects in that regard. Regarding prevention and intervention efforts, encouraging social contact with non-radicals is important, as is addressing online activities and preventing criminal involvement at an early age. Limitations of the study include potential biases in the used data sources and the focus on individuals who have come into contact with law enforcement. Future research should include a broader range of factors (e.g., self-control) and levels (e.g., group level), and would benefit from including interviews with terrorist suspects and data from other countries. Finally, based on the heterogeneity within the terrorist suspect population, tailor-made approaches are needed to continue effectively tackling the phenomenon of terrorism.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».