Ministry of Security and Justice

Abstract People's life trajectories and family dynamics have become increasingly complex, bringing shifts in fertility, gender equality, work-life balance, and economic stability. The scientific challenge is to capture these complexities and understand their causes and consequences. The Generations and Gender Survey (GGS) provides key insights into these developments, serving as a vital resource for researchers and policymakers. The GGS is a cross-national longitudinal survey collecting data on individuals aged 18 to 79 across topics such as union formation, fertility, household dynamics, work, well-being, and gender attitudes. Its key strengths include cross-national comparability, a panel design with prospective and retrospective data, large sample sizes, and multi-mode data collection. The GGS consists of two rounds: GGS-I, launched in the early 2000s in over 20 countries, established a foundation for comparative research, while GGS-II, started in 2020, builds on GGS-I with methodological innovations such as mixed-mode data collection and refreshed samples. This data brief provides an overview of the GGS, outlining its development, design, implementation, and key features. It also highlights the survey's contributions to demographic and social research, and explores the new research opportunities emerging with GGS-II.

Organizations and enterprises search for ways to exploit the vast amount of data that is produced by citizens, sensors, devices and administrative processes. Capitalizing on the produced data should be done responsibly by preventing, mitigating and managing undesired side effects such as violation of rules and regulations, human rights, ethical principles as well as privacy and security requirements. A key challenge in employing data, algorithms and data-driven systems is to adhere to the principle of fairness and justice. In this contribution we focus on the issue of algorithmic fairness, which itself can be framed as a sociotechnical system with interacting social and technical/formal subsystems. Information is a key construct of any sociotechnical system, where information creation and exchange can ease the opacity of interactions between the social and formal subsystems, and of interactions between the subsystems and the environment in which they operate. Based on literature, we categorize the types and flows of the information construct within the sociotechnical systems of algorithmic fairness in 7 categories. As such, the presented insights about the 7 categories of the information construct can form a common mental model whereby social and technical disciplines can inform each other systematically and align their views on algorithmic fairness.

Minorities who face negative interactions with other groups may experience both an increased identification with their ethnic group and a decreased identification with a majority group. First, we argue that it is important to understand the associations minorities have with negative interactions with other groups, especially among minorities who are rarely included in empirical research. Muslim refugees are often treated differently and experience more negative interactions than other immigrants. Second, we combine insights (a) about the role of religiosity in acculturation, suggesting that religiosity does not have positive effects on well-being in societies that do not value (a specific) religion (the religiosity-as-social-value hypothesis), and (b) insights from rejection (dis)identification models, to understand which aspects of being Muslim are associated with discrimination, (dis)identification, and well-being. The central research question is whether the religious practices of Muslims, but not their religious beliefs, coping mechanisms, or principles, are associated with increased perceived discrimination and identification, suggesting that this is due to the high visibility of such practices. Data from Islamic Afghan refugees in the Netherlands (N = 183) shows that only religious practices are positively related to perceived discrimination. Perceived discrimination intensifies the relationship between religious practices and disidentification with the majority group, as well as the relationship between religious practices and well-being. We propose that the visibility of an individual’s religious behavior is relevant to the outcomes of the acculturation process.

ABSTRACT In most models on terrorism, interaction with other radicals is a key factor. However, systematic empirical evidence that radical ties affect behaviour is scarce. Our access to detailed police information allows us to apply Social Network Analysis (SNA) on a Dutch Salafi-Jihadi network and analyse the social relationships and network positions of 80 actors, out of whom 20 turned to terrorist acts. The results suggest that ties to leaders and terrorist offenders, co-attendance of radicalising settings and greater structural embeddedness relate to the likelihood of individual actors to turn to acts of terrorism. However, we also find some individual attributes that may offer competing explanations. In this paper we discuss our findings and address how future research may provide further insights into an important issue for agencies involved in countering terrorism: which radical actors, if any, are more likely to turn to acts of terrorism?

The current study examined whether supervision following release from a Dutch prison in 2012 or 2013 relates to reoffending both during and after the supervision period. A combination of matching by variable and Mahalanobis distance matching was used to match supervised persons to persons who were not supervised after release. Multi-state survival models and zero-truncated negative binomial regressions were used to study the risk and frequency of reoffending. The results suggest that being under supervision following release from prison in the Netherlands is not related to the risk and frequency of reoffending either during or after the supervision period. Possible explanations are discussed and directions for future research are explored.

ObjectivesIn Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space–time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster.MethodsTwo years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space–time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster.ResultsEight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters.ConclusionOur findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

A core principle of privacy protection is to minimize the amount of personal data in data sets to the level needed for the intended usage. The rapid growth of data and data-driven applications demands for using efficient software tools to minimize personal data to the needed level. However, applying Personal Data Minimization (PDM) tools into practice and embedding PDM technology within organizations are challenging tasks. These challenges stem from PDM complexity, context-dependency, multi-disciplinary nature, as well as liability and accountability burdens. This paper aims at enhancing the use of PDM technology within public organizations. To realize this enhancement, we identify three directions – namely, improving usability (efficiency and ease of use), improving trust in PDM tools, and identifying the other influential PDM technology adoption factors. These directions are based amongst others on a literature study and expert interviews. We conducted a questionnaire-based survey among academia and research institutions to investigate the need for PDM technology and the relevancy of the directions empirically. Based on the insights gained, the paper suggests several solution directions and/or avenues for future research. Specifically, we highlight the need for developing customized PDM tools and usage instructions for these tools in different data-sharing settings to facilitate the usability of PDM technology. For establishing trust in PDM technology, we highlight the need for employing various mechanisms such as certification, standardization, and open-source software tools. Thirdly, we call for investigating all factors that are influential in PDM technology adoption to set the usability and trust factors in perspective.

Emotional and behavioural disturbances accompanying neurocognitive disorders may sometimes lead to a criminal offence. Our knowledge of this specific forensic subpopulation is lagging behind the knowledge on, and attention for, 'classic' psychiatric disorders in forensic populations. To gain knowledge on the prevalence and characteristics of individuals with neurocognitive disorders in the forensic population. This retrospective database study uses an anonymised data-set of the National Database of penitentiary psychiatric centres (PPC) (N = 8391), which collects data on all patients admitted to one of the four PPCs (mental health clinics within the prison system) in The Netherlands since May 2013. Inclusion criterion for this study was the presence of a Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR) or Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnostic code belonging to the category of neurocognitive disorders. A DSM-IV-TR or DSM-5 diagnostic code of a neurocognitive disorder was classified in 254 out of 8391 unique individuals, resulting in a prevalence of 3.0% in the total PPC population. The most prevalent diagnosis was unspecified neurocognitive disorder (59.1%). The neurocognitive disorder group significantly differed from a random control group from the database (n = 762) on demographic, clinical and criminological variables. The prevalence of neurocognitive disorders in this real-world clinical sample is remarkably lower than in two earlier studies in similar populations. Also remarkable is the relatively high prevalence of an unspecified neurocognitive disorder. These findings lead us to hypothesise that neurocognitive disorders may be underdiagnosed in this population. Forensic psychiatric settings should evaluate whether they have sufficient expertise available in neuropsychological assessment.

Establishing the path or trajectory of a fired bullet is an often recurring part of shooting incident reconstruction. The current study describes how gravitational pull causes a systemic error on the vertical component of a trajectory reconstruction. Bullet drop, drop angle, and vertical offset are explained and calculated for 10 different handgun/ammunition combinations over a range of distances up to 100 m. The presented results are intended to provide forensic firearm examiners with a reference frame for the magnitude of error introduced on handgun bullet trajectory reconstructions over distance. Threshold values of 20 and 30 m are proposed as conservative distances up to where bullet trajectories can be modeled as straight lines with subsonic/transonic handgun bullets and with supersonic handgun bullets respectively. Both the bullet drop and vertical offset will be below 5 cm at these distances for those categories. The drop angle will be below 0.3°.

We designed a Network Exfiltration Detection System (NEDS) to detect data exfiltration as occurring in ransomware attacks. The NEDS operates on aggregated metadata, which is more privacy-friendly and allows analysis of large volumes of high-speed network traffic. The NEDS aggregates metadata from multiple, sequential sessions between pairs of hosts in a network, which captures exfiltration by both stateful and stateless protocols. The aggregated metadata include averages per session of both packet count, request entropy, duration, and payload size, as well as the average time between sequential sessions and the amount of aggregated sessions. The NEDS applies a number of autoencoder models with unsupervised learning to detect anomalies, where each autoencoder model targets different protocols. We trained the autoencoder models with real-life data collected at network sensors in the National Detection Network as operated by the National Cyber Security Centre in the Netherlands, and configured the detection threshold by varying the false positive rate. We evaluated the detection performance by injecting exfiltration over different channels, including DNS tunnels and uploads to FTP servers, web servers, and cloud storage. Our experimental results show that aggregation significantly increases detection performance of exfiltration that happens over longer time, most notably, DNS tunnels. Our NEDS can be applied to detect exfiltration either in near-real-time data analysis with limited false positive rates, or in captured data to aid in post-incident analysis.