Risk assessment plays an important role in forensic mental health care. The way the conclusions of those risk assessments are communicated varies considerably across instruments. In an effort to make them more comparable, Hanson, R. K., Bourgon, G., McGrath, R., Kroner, D. D., Amora, D. A., Thomas, S. S., & Tavarez, L. P. [2017. A five-level risk and needs system: Maximizing assessment results in corrections through the development of a common language. The Council of State Governments Justice Center. https:// csgjusticecenter.org/wp-content/uploads/2017/01/A-Five-Level-Risk-and-Needs-system_Report.pdf] developed the Five-Level Risk and Needs System, placing the conclusions of different instruments along five theoretically meaningful levels. The current study explores a Five-Level Risk and Needs system for violent recidivism to which the numerical codings of the HCR-20 Version 2 and its successor, the HCR-20V3 are calibrated, using a combined sample from six previous studies for the HCR-20 Version 2 (n = 411 males with a violent index offence) and a pilot sample for the HCR-20V3 (n = 66 males with a violent index offence). Baselines for the five levels were defined by a combination of theoretical (e.g. expert meetings) and empirical (e.g. literature review) considerations. The calibration of the HCR-20 Version 2 was able to detect four levels, from a combined level I/II to an adjusted level V. The provisional calibration of the HCR-20V3 showed a substantial overlap with the HCR-20 Version 2, with each level boundary having a 2-point difference. Implications for practice and future research are discussed.
The purpose of this study is to analyze the relationship between sustainable performance and risk management, whereby sustainability (innovation), interdisciplinarity and leadership give new insights into the traditional perspectives on performance and risk management in the field of accounting and finance.
MULTIFILE
Purpose: The purpose of this study is to find determinants about risk resilience and develop a new risk resilience approach for (agricultural) enterprises. This approach creates the ability to respond resiliently to major environmental challenges and changes in the short term and adjust the management of the organization, and to learn and transform to adapt to the new environment in the long term while creating multiple value creation. Design/methodology: The authors present a new risk resilience approach for multiple value creation of (agricultural) enterprises, which consists of a main process starting with strategy design, followed by an environmental analysis, stakeholder collaboration, implement ESG goals, defining risk expose & response options, and report, learn & evaluate. In each step the organizational perspective, as well as the value chain/area perspective is considered and aligned. The authors have used focus groups and analysed literature from and outside the field of finance and accounting, to design this new approach. Findings: Researchers propose a new risk resilience approach for (agricultural) enterprises, based on a narrative about transforming to multiple value creation, founded determinants of risk resilience, competitive advantage and agricultural resilience. Originality and value: This study contributes by conceptualizing risk resilience for (agricultural) enterprises, by looking through a lens of multiple value creation in a dynamic context and based on insights from different fields, actual ESG knowledge, and determinants for risk resilience, competitive advantage and agricultural resilience.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
"Rising Tides, Shifting Imaginaries: Participatory Climate Fiction-Making with Cultural Collections," is an transdisciplinary research project that merges information design, participatory art, and climate imaginaries to address the pressing challenge of climate change, particularly the rising sea levels in the Netherlands. The doctoral research project aims to reimagine human coexistence with water-based ecosystems by exploring and reinterpreting audiovisual collections from various archives and online platforms. Through a creative and speculative approach, it seeks to visualize existing cultural representations of Dutch water-based ecosystems and, with the help of generative AI, develop alternative narratives and imaginaries for future living scenarios. The core methodology involves a transdisciplinary process of climate fiction-making, where narratives from the collections are amplified, countered, or recombined. This process is documented in a structured speculative archive, encompassing feminist data visualizations and illustrated climate fiction stories. The research contributes to the development of Dutch climate scenarios and adaptation strategies, aligning with international efforts like the CrAFt (Creating Actionable Futures) project of the New European Bauhaus program. Two primary objectives guide this research. First, it aims to make future scenarios more relatable by breaking away from traditional risk visualizations. It adopts data feminist principles, giving space to emotions and embodiment in visualization processes and avoiding the presentation of data visualization as neutral and objective. Second, the project seeks to make scenarios more inclusive by incorporating intersectional and more-than-human perspectives, thereby moving beyond techno-optimistic approaches and embracing a holistic and caring speculative approach. Combining cultural collections, digital methodologies, and artistic research, this research fosters imaginative explorations for future living.
