Social Research Research Articles

The development and evolution of ethics review boards – Israel as a case study

Although well established in developed countries, Ethics review boards in the academia, and specifically for social and behavioral sciences (SBS) research, is a relatively new, and still a controversy inducing endeavor. This study explores the establishment and functioning of ERBs in Israeli academia, serving as a case study for the challenges and progress made in ensuring ethical research practices in non-medical related spheres. A purposeful sample of 46 participants was selected, comprising ERB current or past members and SBS researchers, who each interacted extensively with ERB’s evaluation processes. The participants came from all eight research universities of Israel, as well as seven large public and private academic colleges. Open, semi structured In-depth interviews were conducted in order to facilitate a critical reflective stance among the various participants, exploring their experiences, behaviors and the meanings they assign to ERB processes. Our research revealed two major themes. The first is the developmental trajectories in the construction processes of ERBs in Israel, comprised of the following stages: Initiation, expansion, opposition, and acceptance and assimilation. The second was a typology of participation throughout the ERBs’ construction process. Inspired by Merton’s strain theory, this typology includes conformists, ritualists, ideologists, control-freaks, and insurgents. The findings expose a unique perspective into the initiation, establishment, and development of ERBs, which can shed light on the role of ERBs in general, as well as point out how resistance to ERBs, as well as the different reasons for championing them, affected not only ERBs form and their legitimacy, but also their interaction with researchers.

Social Entrepreneurship, Empowerment of Women Experiencing Homelessness and Gender Equality

The relationship between social entrepreneurship and social change continues to generate interest among researchers. We apply Kabeer’s conceptualisation of empowerment and additionally, explore intersectionality, paradox of empowerment and change agents for gender equality, to consider the practices of a social enterprise supporting the empowerment of women experiencing homelessness in the UK. We reflect on the motivations and drivers behind its work, the extent to which the women were able to access and control resources, and the change agents enabling this transformation. While the practices of the social enterprise in our case study positively affected women’s empowerment, they also bring to the fore several areas for reflection for social entrepreneurship research; the incorporation of intersectionality into advocacy for marginalised women such as those in our study, the role of male leaders in advancing gender empowerment and the need to find the delicate balance between empowerment on the one hand, and paternalism and suppression on the other.

Artificial Intelligence and Scientific Research:Prospects and Risks———Synthesis of the session “Artificial Intelligence and Paradigm Change in Science and Technology Innovation” in Tianjin Forum 2023

In the sub-forum “Transformation of Artificial Intelligence and Paradigm in Science and Technology Innovation” of Tianjin Forum 2023, scholars from both China and abroad discussed the impact of artificial intelligence. With the deepening development of the new technological revolution and industrial transformation, new-generation artificial intelligence technology is continuously making breakthroughs in research and application. AI not only promotes the transformation of material productivity, but also gradually emerges as a critical engine for enhancing the knowledge productivity. Since the emergence of the concept of "AI for Science", it has become an obvious proposition generally accepted by the academic community for its tremendous enabling capabilities for knowledge production. Artificial intelligence technology continues to achieve breakthroughs and gain widespread infiltration into the scientific research field, introducing new elements and momentum into scientific research and significantly catalyzing the enhancement of scientific research efficiency and paradigm shifts. AI-driven scientific research has become a new frontier in the global application of artificial intelligence. However, as artificial intelligence triggers paradigm shifts in social science research, the issues of data security, ethics, and value alignment that it brings about need to draw attention from the social science community. Scholars participating in the forum generally concurred that, in regulating the enabling role of AI in scientific research, it is imperative to take into account the specificities of various disciplines and stages, and comprehensively reasonable rational risk allocation mechanisms, platform. support mechanisms, and collaborative participation frameworks to achieve prudent, agile, and full lifecycle regulation of AI for Science.

Risk and Protective Factors for Bullying in Sport: A Scoping Review

AbstractThe aim of the current study was to examine risk and protective factors related to bullying in sport. Adopting the methodological approach outlined by Arksey and O’Malley (International Journal of Social Research Methodology 8(1):19–32, 2005), 37 articles met the inclusion criteria. A consistent definition of bullying could not be identified in the publications examined, and several articles (n = 8) did not explicitly define bullying. The most frequent risk factor identified was an individual’s social background (n = 9). Negative influence of coaches (n = 5), level of competition (n = 5), lack of supportive club culture (n = 5) and issues in locker rooms (n = 4) were among the most commonly cited risk factors for bullying in sport settings. Preventative policies were cited as the most common method to reduce the incidence of bullying (n = 13). Contextually tailored intervention programmes (n = 5) were also noted as a key protective factor, particularly for marginalised groups, including athletes with disabilities or members of the LGBTQ+ community. The need for sport-specific bullying prevention education was highlighted by 10 of the articles reviewed. In summary, the current review accentuates the range of risk and protective factors associated with sport participation. Furthermore, the need for educational training programmes to support coaches in addressing and preventing bullying within sport settings is emphasised.

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain.
 OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions.
 METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities.
 RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster.
 CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.

Importance of patient and public involvement in doctoral research involving people living with dementia.

There is increasing recognition of the need to include patients and the public in the research process. There is extensive literature about patient and public involvement (PPI) in research, but fewer articles report on PPI in doctoral research. To reflect on establishing an advisory group for a doctoral study, exploring the opportunities and challenges associated with including patients with dementia in the research process. The authors discuss the practicalities of establishing an advisory group, the challenges of being a novice researcher, long-term commitment to PPI, the overall approach to PPI and ethical considerations. Establishing an advisory group for a doctoral study can facilitate mutual learning and enhance the study's quality. Achieving high-quality PPI in health and social care research can ultimately improve its quality and relevance. An important aspect of the doctoral journey is developing knowledge and skills to facilitate PPI as part of a researcher's apprenticeship.

The Crime Prediction of Criminal Activity Based on Weather Changes Towards Quality of Life

Crime is a significant problem in society, and crime prevention is crucial. Factors such as politics, economics, culture, education, demographics, and employment have been identified as contributing to crime. Recent studies have also explored the relationship between weather and crime. Therefore, this research aims to identify the best-performing machine learning algorithm based on weather in Malaysia, using crime data from the Royal Malaysia Police and Meteorological Department from 2011 to 2020. Five machine learning algorithms were utilized, and the results showed that all algorithms had good prediction accuracy, with Gradient Boosted Trees performing the best, with an error rate of less than 23%. Location was found to be the most important feature in all the models. This study provides a valuable fundamental framework for environmental crime and social impact research scholars to conduct a more in-depth analysis of the prediction models. This study establishes a fundamental framework for scholars in environmental crime and social impact research to conduct in-depth analysis using prediction models, thereby contributing to a better understanding of the complex relationship between weather and crime, and aiding in the development of effective crime prevention strategies.

Exhibition research and practice at CERN: challenges and learnings of science communication `in the making'

This practice insight paper presents a reflection on a four-year collaboration between science communication practitioners and researchers, using CERN's new education and outreach centre as a case study. The development of interactive exhibitions for this centre was informed by a variety of front-end and formative evaluation studies, from online surveys to exhibit prototype testing. As a multidisciplinary team of exhibition developers and social science researchers, we describe and discuss the challenges of — as well as learnings from — working together. Our experience will be relevant for everyone curious to discover `behind-the-scenes' work of research-informed exhibition development in a large scientific laboratory.

Structural efficiency evaluation of humanities and social sciences research based on MEA model: a case of universities in Jiangsu, China

Structural efficiency evaluation of humanities and social sciences research based on MEA model: a case of universities in Jiangsu, China

‘Shooting in the dark’: implications of the research–practice gap for enhancing research use in adult social care

Background: Despite calls for greater use of research and an appetite to do so within adult social care, a gap persists between research and practice. Aims and objectives: To explore views of adult social care staff about research and its application to everyday practice. To understand how these might impact upon research use capacity-building initiatives within adult social care organisations. Methods: Thematic analysis of semi-structured qualitative interviews with 25 staff members working within the adult social care departments of three English local authorities. Findings: Participants characterised research as feeling separate from practice. They reflected on their use of it in relation to pressures affecting adult social care and identified a lack of relevant research. Research benefiting service users, supporting individual practice or informing organisational decision-making was considered useful. However, research could also be viewed as a luxury where its findings were felt to represent an ‘ideal’ rather than real world of practice or did not accord with practice knowledge or local experience. Discussion and conclusions: While participants feel positively towards research, there remains a gap between these perceptions and its use in practice. There remains a need to improve research relevance and accessibility and to clarify its role in decision-making in social care, including where there is no evidence, where evidence challenges existing practice, or where the evidence base is growing. Collaborative partnerships between adult social care organisations, researchers and service users could help to narrow the research–practice gap and support the routine translation of research to practice.

Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior --- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.

SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models

Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. Taking inspiration from social science research, we start with a documented list of 93 US-centric stigmas and curate a question-answering (QA) dataset which involves simple social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts, with a variety of prompt styles, carefully constructed to systematically test for both social bias and model robustness. We present results for SocialStigmaQA with two open source generative language models and we find that the proportion of socially biased output ranges from 45% to 59% across a variety of decoding strategies and prompting styles. We demonstrate that the deliberate design of the templates in our benchmark (e.g., adding biasing text to the prompt or using different verbs that change the answer that indicates bias) impacts the model tendencies to generate socially biased output. Additionally, through manual evaluation, we discover problematic patterns in the generated chain-of-thought output that range from subtle bias to lack of reasoning. Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.

Enhancing Learning Outcomes in Social Sciences through the Integration of Research-Based Teaching Strategies

In the dynamic landscape of social sciences education, the integration of research-based teaching strategies stands as a transformative approach to enriching learning outcomes and cultivating critical thinking skills among students. This abstract elucidates the profound impact of infusing research-based methodologies into social sciences pedagogy, highlighting the multifaceted benefits and implications for educators and learners alike. Research-based teaching strategies epitomize a pedagogical paradigm rooted in empirical evidence and scholarly inquiry, aimed at facilitating deeper understanding and engagement with course content. Within the realm of social sciences, where complex societal phenomena and interdisciplinary perspectives abound, the integration of such strategies offers a strategic pathway to navigate the intricacies of the discipline and foster intellectual curiosity among students. Central to the efficacy of research-based teaching strategies is their ability to stimulate active learning and student-centred exploration of subject matter. By incorporating experiential learning activities, inquiry-based projects, and collaborative research endeavors, educators empower students to construct knowledge collaboratively and apply theoretical frameworks to real-world contexts. Through hands-on engagement with primary sources, data analysis, and critical reflection, students develop the analytical acumen and problem-solving skills essential for navigating the complexities of contemporary social issues. Furthermore, research-based teaching strategies serve as catalysts for cultivating a culture of inquiry and intellectual rigor within the classroom environment. By encouraging students to critically evaluate evidence, interrogate assumptions, and engage in disciplined inquiry, educators foster a spirit of intellectual curiosity and scholarly inquiry among learners. Through exposure to diverse methodological approaches and research methodologies, students gain insight into the nuances of social scientific inquiry and develop the capacity to formulate evidence-based arguments and perspectives. Moreover, empirical research underscores the positive impact of integrating research-based teaching strategies on learning outcomes and student engagement in social sciences education. By aligning instructional practices with evidence-based principles of effective teaching and learning, educators can optimize student retention, mastery of content, and academic achievement.

The Homo Economicus as a Prototype of a Psychopath? A Conceptual Analysis and Implications for Business Research and Teaching

AbstractSince the beginning of business research and teaching, the basic assumptions of the discipline have been intensely debated. One of these basic assumptions concerns the behavioral aspects of human beings, which are traditionally represented in the construct of homo economicus. These assumptions have been increasingly challenged in light of findings from social, ethnological, psychological, and ethical research. Some publications from an integrative perspective have suggested that homo economicus embodies to a high degree dark character traits, particularly related to the construct of psychopathy, representing individuals who are extremely self-centered and ruthless, without feelings of remorse or compassion. While a growing body of research notes such a similarity on a more or less anecdotal basis, this article aims to explore this connection from a more rigorous perspective, bridging insights from psychological, economic, and business research to better understand the potentially dark traits of homo economicus. The analysis shows that homo economicus is not simply some kind of psychopath, but specifically a so-called subclinical or Factor 1 psychopath, who is also referred to as a “corporate psychopath” in business research. With such an analysis, the paper adds an additional perspective and a deeper psychological level of understanding as to why homo economicus is often controversially debated. Based on these insights, several implications for academic research and teaching are discussed and reflected upon in light of an ethics of virtue and care.

Economic Empowerment for Enhanced Health Equity: A Qualitative Study of Women Living with HIV in Wakiso District, Uganda

Women living with HIV in Sub-Saharan Africa struggle for health equity while also facing the constraints of economic hardship and patriarchy. In this article, we describe the experiences of women who participated in economic empowerment groups for women living with HIV in Wakiso district, Uganda. Fifty women engaged in economic empowerment groups and ten social worker and community health worker key informants enrolled in the study. Taking a phenomenological approach, we used field notes, qualitative interviews, and focus groups with participants and key informants to describe the experiences of the women who joined the groups. We present the findings including enhanced management of HIV and increased property and business ownership due to economic empowerment group participation. We discuss the findings through an economic empowerment framework and offer implications for social work interventions and research.

Multi-measures to Promote Rural Revitalization Mainly based on the Transfer of Homestead Land

This paper takes Liu Laoqiao Village, a village of Taihe County with characteristics of protection category, as an example, adapts to the local conditions, clarifies the characteristics and positioning of the village through the system of social research methods, and analyzes the development results achieved by the village. On this basis, the main idea is to optimize the layout of village development with the reasonable revitalization of the residence base, to promote the combination of planting and raising and industrial upgrading, to grow the characteristic industries such as leisure agriculture, countryside tourism, and lodging economy, and to vigorously develop the planting and breeding industries. Practically solve the actual problems of the village, adhere to the characteristics of the revitalization of the road, so that the village to achieve a long-lasting and effective revitalization of the effect of the village for the development of the village, for the villagers to seek happiness, and to achieve a village with a number of villages, multi-village common development.

Polygenic Indices (aka Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)—the new recommended label for polygenic scores in social science applications—are genetic summary scales often used to represent an individual’s liability for a disease, trait, or behavior on the basis of the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science data sets have facilitated increased uptake of PGIs in social science research, a trend that will likely continue. Yet most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, I provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. I summarize recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. I conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

PASTORALIST MOBILITY ALONG THE SUDANESE ETHIOPIAN BORDERLAND: TOWARDS COOPERATIVE LAND MANAGEMENT

This study followed an interdisciplinary mixed approach that consisted of social research methods and geospatial technology to investigate the livestock mobility of four pastoralist groups from Sudan and two Ethiopian pastoralist groups who fled to Sudan due to the conflict in the Tigray region. The study area is a shared borderland located between the two countries and is part of Gadarif State in Sudan and the Tigray and Amhara regions in Ethiopia. The findings of the study showed that there are diverse types of pastoral mobility in the borderland, ranging from short and circular movement to wide and directional type of mobility in response to seasonal variations, the spread of mechanised farming and the eruption of insecurity events. Despite facing numerous challenges including the governments’ restrictions on cross-border movement and the prevalence of looting and rustling, trans-boundary mobility is practised by most pastoralist groups in the borderland. Pastoralist leaders are playing an important role in facilitating cross-border mobility. The recent conflict in the Tigray region forced many Ethiopian pastoralists to flee to Sudan with their animals, and they have had to cope with this situation by reducing the number of head they own and limiting their mobility to a more confined area. Despite competing claims by both governments, there is a pressing need for a cooperative land management policy applying soft border arrangements that incorporate customary land use rights for pastoralists from both sides, instead of the hardline border policies currently in place. This article was published open access under a CC BY-NC 4.0 licence: https://creativecommons.org/licenses/by-nc/4.0/ .

The postwar Columbia tradition in sociology: Its cognitive commonalities and social mechanisms

For a period of some three decades (1940s–1970s) the faculty and graduate students at the Department of Sociology at Columbia University, and its associated research unit (the Bureau of Applied Social Research: BASR), successfully produced a stream of innovative sociological studies which was particularly important in building on the foundations of classical sociology to establish modern sociology. Modern Sociology was produced as a theoretically sophisticated scientific enterprise firmly based on a solid empirical foundation produced by appropriate social research methods (as master-minded by Paul F. Lazarsfeld). The ‘Columbia Tradition’ is an approach, rather than being focussed on any particular subject matter, involved the development of ‘middle-range theory’, often broken-out from classic theory (as propounded by Robert K. Merton), and backed up by efforts at knowledge cumulation and institution building. But the School included a glittering array of important sociologists and hosts of others who extended the work of the two leaders, developing it in further directions, as is expected of any school. This school was sustained by the vision of a developing scientific sociology propounded by its founders, but it faltered as its founders retired from active leadership roles, in addition to being impacted by changes in Columbia University and broader Sociology environments.

Practice Standards for Addressing Social Justice in Social Work Research

Practice Standards for Addressing Social Justice in Social Work Research