The article engages with the recent studies on multilevel regulation. The starting point for the argument is that contemporary multilevel regulation—as most other studies of (postnational) rulemaking—is limited in its analysis. The limitation concerns its monocentric approach that, in turn, deepens the social illegitimacy of contemporary multilevel regulation. The monocentric approach means that the study of multilevel regulation originates in the discussions on the foundation of modern States instead of returning to the origins of rules before the nation State was even created, which is where the actual social capital underlying (contemporary) rules can be found, or so I wish to argue. My aim in this article is to reframe the debate. I argue that we have an enormous reservoir of history, practices, and ideas ready to help us think through contemporary (social) legitimacy problems in multilevel regulation: namely all those practices which preceded the capture of law by the modern State system, such as historical alternative dispute resolution (ADR) practices.
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ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
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Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency & explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency & explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling.Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.
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This paper examines the network governance approach of the Dutch Urban Envoy in the context of multilevel governance in the European Union. This paper aims to answer the research question on how the scope of network governance can explore the performance of the Dutch Urban Envoy. By analyzing network characteristics, such as legitimacy, actor-level properties, and network-level properties, this paper seeks to provide a nuanced understanding of the performance of the Dutch Urban Envoy. Drawing on previous research, this paper identifies the applicability and limitations of assessing network characteristics in understanding advocacy processes. The paper successfully visualizes the networks of the Dutch Urban Envoy and explores their roles and mandates, contributing to determining the added value of their position. However, the network governance approach has limitations in explaining the tangible successes and challenges of the Dutch Urban Envoy that cannot be directly attributed to their overall performance.
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Symposiumbijdrage conferentie EARLI SIG 14, 11-14 september 2018, Genève Learning across the contexts of school and the workplace is highly relevant to the VET-sector. This contribution analyses these cross-contextual learning processes with three key issues in mind: (1) guidance by vocational educators, (2) assessment of students’ development and (3) design of VET-learning environments. Guidance, assessment and overarching VET-curriculum designs form the basis for constructive alignment as an approach to optimize conditions for high quality cross-contextual learning processes. We used the theoretical framework of boundary crossing to clarify the complex, multilevel nature of these key issues.
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The workshop aims to understand how a living lab network structures contribute to system innovation. Living labs as system innovation initiatives can substantially alter established network structures. Moreover, structures can undergo alterations through subtle interventions, with impact on the overall outcomes of living labs. To understand how such change occurs, we develop a multilevel network perspective to study collaborations toward system innovation. We take this perspective to help understand living lab dynamics, drawing on innovative examples and taking into consideration the multilayered structures that the collaboration comprises.
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Presentatie van het manuscript 'COVID 19 Conspiracy Thinking Across the World ' door Annemarie Walter en Hugo Drochon bij REPRESENT, Research Centre for the Study of Parties and Democracy van de Universiteit van Nottingham, op 24 maart 2021.
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Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.
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Multilevel models using logistic regression (MLogRM) and random forest models (RFM) are increasingly deployed in industry for the purpose of binary classification. The European Commission’s proposed Artificial Intelligence Act (AIA) necessitates, under certain conditions, that application of such models is fair, transparent, and ethical, which consequently implies technical assessment of these models. This paper proposes and demonstrates an audit framework for technical assessment of RFMs and MLogRMs by focussing on model-, discrimination-, and transparency & explainability-related aspects. To measure these aspects 20 KPIs are proposed, which are paired to a traffic light risk assessment method. An open-source dataset is used to train a RFM and a MLogRM model and these KPIs are computed and compared with the traffic lights. The performance of popular explainability methods such as kernel- and tree-SHAP are assessed. The framework is expected to assist regulatory bodies in performing conformity assessments of binary classifiers and also benefits providers and users deploying such AI-systems to comply with the AIA.
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The prediction of mechanical elastic response of laminated hybrid polymer composites with basic carbon nanostructure, that is carbon nanotubes and graphene, inclusions has gained importance in many advanced industries like aerospace and automotive. For this purpose, in the current work, a hierarchical, four-stage, multilevel framework is established, starting from the nanoscale, up to the laminated hybrid composites. The proposed methodology starts with the evaluation of the mechanical properties of carbon nanostructure inclusions, at the nanoscale, using advanced 3D spring-based finite element models. The nanoinclusions are considered to be embedded randomly in the matrix material, and the Halpin-Tsai model is used in order to compute the average properties of the hybrid matrix at the lamina micromechanics level. Then, the standard Halpin-Tsai equations are employed to establish the orthotropic elastic properties of the unidirectional carbon fiber composite at the lamina macromechanics level. Finally, the lamination theory is implemented in order to establish the macroscopic force-strain and moment-curvature relations at the laminate level. The elastic mechanical properties of specific composite configurations and their performance in different mechanical tests are evaluated using finite element analysis and are found to considerably increase with the nanomaterial volume fraction increase for values up to 0.5. Further, the hybrid composite structures with graphene inclusions demonstrate better mechanical performance as compared to the identical structures with CNT inclusions. Comparisons with theoretical or other numerical techniques, where it is possible, demonstrate the accuracy of the proposed technique.
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