Exploring social workers' views on assessing child neglect in England and Wales

Child neglect poses many issues for social work, notably in terms of effective assessment leading to informed intervention targeting the needs of children and families. In response to this challenge, our multiphase research project is developing a new multiagency child neglect measurement tool. The phase of the project reported in this article administered an online survey via Qualtrics to explore the views of children and families social workers on assessing child neglect. One hundred and twenty-nine completed responses were received from registered children and families social workers in England and Wales. The main findings are that social workers are regularly undertaking child neglect assessments and feel relatively confident in completing them. They also feel relatively confident that their assessments are inclusive of social harms such as poverty and social isolation, but less confident they are accurate and informed by research evidence. Almost two-thirds are using a child neglect assessment tool, but they lack confidence in these accurately assessing neglect or being quick and simple to use. The findings illustrate that social workers require both the work conditions and tools to use in which they feel confident to undertake balanced and accurate assessments of child neglect

Umwelt in der Politikwissenschaft

Zusammenfassung: Was ist Politikwissenschaft? Was verstehen wir unter politikwissenschaftlicher Umweltforschung? Welche Forschungsbereiche, Institutionen und Studienmöglichkeiten gibt es und mit welchen Themen hat sich die politikwissenschaftliche Umweltforschung in den letzten Jahren beschäftigt? Diese und weitere Fragen stehen im Vordergrund dieses Überblickartikels, der zum Ziel hat, einen einfachen Einstieg in das Fachgebiet und zentrale Themenkomplexe zu bieten sowie auf aktuelle Trends, Chancen und Herausforderungen der Inter- und Transdisziplinarität auf diesem Gebiet einzugehen. Mit Blick auf die Entwicklung der politikwissenschaftlichen Umweltforschung in den letzten Jahren ist, nicht zuletzt aufgrund der zunehmenden Dynamik politikrelevanter Umweltherausforderungen, zu erwarten, dass die Bedeutung des Fachs weiter zunehmen wird. Um zum Verständnis politischer Strukturen, Prozesse und Inhalte im Umweltkontext beitragen zu können, gewinnt die effektive Zusammenarbeit mit Wissenschaftlerinnen und Wissenschaftlern aus anderen Fächern aufgrund der Komplexität des Themenbereichs kontinuierlich an Bedeutung

Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation

The detection of community structure in networks is intimately related to finding a concise description of the network in terms of its modules. This notion has been recently exploited by the Map equation formalism (M. Rosvall and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an information-theoretic description of the process of coding inter- and intra-community transitions of a random walker in the network at stationarity. However, a thorough study of the relationship between the full Markov dynamics and the coding mechanism is still lacking. We show here that the original Map coding scheme, which is both block-averaged and one-step, neglects the internal structure of the communities and introduces an upper scale, the `field-of-view' limit, in the communities it can detect. As a consequence, Map is well tuned to detect clique-like communities but can lead to undesirable overpartitioning when communities are far from clique-like. We show that a signature of this behavior is a large compression gap: the Map description length is far from its ideal limit. To address this issue, we propose a simple dynamic approach that introduces time explicitly into the Map coding through the analysis of the weighted adjacency matrix of the time-dependent multistep transition matrix of the Markov process. The resulting Markov time sweeping induces a dynamical zooming across scales that can reveal (potentially multiscale) community structure above the field-of-view limit, with the relevant partitions indicated by a small compression gap.Comment: 10 pages, 6 figure

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Climate action through policy expansion and/or dismantling: Country-comparative Insights: An introduction to the Special Issue

Elected politicians and civil servants are key in developing climate policy. The articles in this special issue investigate factors that induce politico-administrative actors to adopt climate policies and dismantle anti-climate policies to advance decarbonisation. Politico-administrative actors have predominantly expanded climate policy and raised policy ambition in recent decades. However, economic crises and weakening public support may cause dismantling of climate policy and hamper policy ambition. Against this backdrop, articles in this special issue also study factors that propel climate policy dismantling. Together, the contributions show that interactions between politico-administrative actors and publics, organised interests, and international organisations shape climate and anti-climate policy change

Climate policy ambition: Exploring a policy density perspective

National policy ambition plays a central role in climate change governance under the Paris Agreement and is now a focus of rapidly emerging literature. In this contribution, we argue that policy ambition can be captured by the level of national policy activity, which in accordance with the existing literature should be referred to as “policy density.” In this study, we measure climate policy density by drawing on three publicly available databases. All three measurements show an upward trend in the adoption of climate policy. However, our empirical comparison also reveals differences between the measurements with regard to the degree of policy expansion and sectoral coverage, which are due to differences in the type of policies in the databases. For the first time, we compare the patterns of policy density within each database (2000–2019) and reveal that while they are different, they are nonetheless potentially complementary. Since the choice of the database and the resulting measurement of policy density ultimately depend on the questions posed by researchers, we conclude by discussing whether some questions are better answered by some measurements than others