5112 research outputs found
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Police Interviewing
No full textIn the UK and elsewhere, police interviewing with victims, witnesses and suspects of crime is an integral part of the criminal justice process, intending to gather plentiful and accurate information from interviewees related to criminal investigations (Bull, Diversity in harmony—Insights from psychology: Proceedings of the 31st International Congress of Psychology, pp. 191–210, Wiley, 2018; College of Policing, Investigative interviewing, https://www.app.college.police.uk/app-content/investigations/investigative-interviewing/, 2013; Redlich et al., Applied Cognitive Psychology, 28, 817–828, 2014). As such, methods that facilitate good quality communication in these settings are imperative to meet this aim, and a great deal of research has been conducted in the past few decades to identify best practices in this setting. This chapter will explore these advancements and provide an overview of key police interviewing models, such as accusatorial and information-gathering approaches, and detail the psychological concepts that underpin these concepts, such as coercion, false confessions, rapport-building and empathy, and cognition. This chapter will also highlight the use of psychological tools within these approaches, such as methods of detecting deception and cognitive techniques that facilitate memory recall and communication (e.g., the cognitive interview). While the chapter has a strong focus on suspect interviewing, it will also discuss police interviewing practices with witnesses and victims
Galaxy Morphology Classification using Deep Learning: A Compact Survey
No full textGalaxy morphology classification is a key task in extragalactic astronomy, as structural and dynamical properties of galaxies are closely tied to their formation history, stellar populations, and environment. Next-generation surveys such as the Sloan Digital Sky Survey (SDSS), the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), and the Euclid mission are producing petabyte-scale imaging data, making manual classification impractical. In view of this, deep learning (DL)-based automated methods provide a scalable and efficient alternative. This survey reviews DL methods for galaxy morphology classification over the past five years focusing on convolutional neural networks (CNNs), vision transformers (ViTs), graph neural networks (GNNs), and hybrid architectures. A taxonomy is introduced to categorize models by architecture, metadata integration, and interpretability, supported by comparative analysis of datasets, generalization ability, and efficiency. This survey highlights several key gaps, including limited cross-survey robustness, underuse of astrophysical metadata (e.g., redshift, Sérsic index, velocity dispersion), insufficient interpretability of learned features such as spiral arms or bulge-to-disk ratios, and high computational cost of advanced architectures. To address these gaps, promising directions involve lightweight and metadata-aware models, multimodal frameworks integrating spectroscopy and imaging, self-supervised and physics-informed methods, and approaches incorporating temporal and spatial evolution. By aligning machine learning progress with astrophysical insight, future models can achieve accuracy, scalability, and scientific interpretability, ultimately advancing our understanding of galaxy structure and evolution
Gender, relationships and desistance
No full textThis edited collection offers unique insight into the role and impact of relationships for women involved in the criminal justice system. Through drawing together academic research, lived experience and reflections of frontline perspectives, the collection interrogates the personal, public and professional themes of these relationships, broadening current analysis and calling for a reimagining of the future. Each author demonstrates the complexity of these themes with rich and powerful contributions that offer a crucial understanding into the complexity and nuance of this area. By connecting a range of perspectives and different forms of expression, this original collection extends and challenges current understanding and calls for reimaging and change
Migration psychology:global dynamics of family, policy, and inclusion
No full textThis book examines the global dimensions of migration psychology, showing how migration shapes family life, policy frameworks, and processes of inclusion and exclusion. As the second volume of a two-part collection, it expands the focus beyond the United Kingdom to highlight the psychological experiences of migrants across diverse international contexts. Chapters explore themes of intergenerational separation, circular migration, language and identity, and the role of cultural practices in sustaining wellbeing. Grounded in psychology and in conversation with sociology, theology, and the arts, the volume demonstrates how migration is both a deeply personal journey and a systemic, political phenomenon. By foregrounding issues of family, belonging, faith, culture, and community, this book offers a timely, multidisciplinary contribution to migration studies. It will be essential reading for scholars, students, and practitioners interested in psychology, migration, diaspora, and the social dynamics of inclusion
New Insights on the Role of Spiritual Leadership in Positive Outcomes in the Scottish Public Sector
No full textSpiritual leadership, a leadership style that focuses on creating vision and values congruence across the strategic, empowered team, and individual levels to achieve positive outcomes, is well suited to supporting employees’ service-oriented behaviours and creativity behaviours in the public sector. However, we still know little about how spiritual leaders shape behaviours in the public sector. Drawing on social learning theory and service linkage research, this article produces novel theoretical insights and tests a model in which spiritual leadership and ethical climate foster conditions to enhance positive service climate and subsequent (a) service-oriented behaviours and (b) employee creative behaviour. The research hypotheses were tested, using evidence from the public sector, on a sample of 400 from Scotland. Results show that all hypotheses were supported
A Hybrid Optimization Approach for Multi-Generation Intelligent Breeding Decisions
No full textMulti-generation intelligent breeding (MGIB) decision-making is a technique used by plant breeders to select mating individuals to produce new generations and allocate resources for each generation. However, existing research remains scarce on dynamic optimization of resources under limited budget and time constraints. Inspired by advances in reinforcement learning (RL), a framework that integrates evolutionary algorithms with deep RL was proposed to fill this gap. The framework combines two modules: the Improved Look-Ahead Selection (ILAS) module and Deep Q-Networks (DQNs) module. The former employs a simulated annealing-enhanced estimation of the distribution algorithm to make mating decisions. Based on the selected mating individual, the latter module learns multi-generation resource allocation policies using DQN. To evaluate our framework, numerical experiments were conducted on two realistic breeding datasets, i.e., Corn2019 and CUBIC. The ILAS outperformed LAS on corn2019, increasing the maximum and mean population Genomic Estimated Breeding Value (GEBV) by 9.1% and 7.7%. ILAS-DQN consistently outperformed the baseline methods, achieving significant and practical improvements in both top-performing and elite-average GEBVs across two independent datasets. The results demonstrated that our method outperforms traditional baselines, in both generalization and effectiveness for complex agricultural problems with delayed rewards
Interval scale
No full textThis article examines the concept and application of interval scales in social science research, highlighting their theoretical foundations, practical applications, methodological challenges, and emerging innovations. Key discussions include debates on Likert-scale assumptions, practical strategies for validating interval measures, and implications of technological advancements such as Big Dataand artificial intelligence. Recommendations emphasize rigorous validation, transparent reporting, and ethical considerations, outlining best practices for enhancing measurement accuracy and reliability. Ultimately, the article stresses the critical role of interval scaling in ensuring rigorous and impactful social science research
