Social Science Community Research Articles

Россия в перспективе исторической социологии: эвристические перспективы и методологические проблемы

The article discusses the heuristic and methodological potential of historical sociology as one of the most dynamically developing disciplines of modern social and scientific knowledge. It is argued that this area of research is functionally capable of taking on the role of today's analytical philosophy of history in the form of integrity and, at the same time, the operational reflection of the historical experience of our country. The article points out the deficient nature of previous conceptualizations of the domestic past from the point of view of historical sociology. First, the development of this discipline in the West is briefly analyzed, starting with the classics of world sociology. The qualitative and quantitative growth of historical and sociological research in the second half of the 20th century—which led to a significant expansion of the explanatory repertoire of macrosociology—is highlighted. Next, the ambivalent position of historical sociology in Russia is discussed, where—despite the existing interest in its methodological categories and substantive results—a full-fledged institutionalization of this sociological discipline has not yet occurred. Thus, a conclusion is made about the existence of a serious local tradition of interest in historical-sociological synthesis, starting with the classics of historiography and pioneers of Russian sociology at the turn of the 19th and 20th centuries. At the same time, substantial progress is highlighted on the part of the domestic social-scientific community in the reception of the achievements of world-historical sociology at the beginning of the 21st century. The next section shows the particular relevance of modern historical-sociological approaches to the problematization of the past in the context of the domestic epistemological situation. A number of the most promising thematic complexes in modern Russian academia are highlighted. In conclusion, we highlight issues concerning framework research and scientific organization faced by historical sociology in Russia.

Democratizing Social Scientists’ Words and Distinctions: Where and Why Moral Reasons Are Decisive

Sitting at your desk, working on your social science paper, you take yourself to be free to use key words and distinctions as you see fit. I argue that these decisions aren’t up to you. Instead, they should be made collectively, by the social science community you’re embedded in, talking to, and contributing to, along with its stakeholders. Such democratic and deliberative processes will be driven by the common good, as opposed to benefiting particular social scientists, schools, research programs, methods, or regions of the world. My book, Words and Distinctions for the Common Good, is an invitation: I invite social science communities to democratically work on how to use their key words and distinctions—and which epistemic and moral goods these uses should help attain.

The State of Marine Social Science: Yesterday, Today, and into the Future.

Rapidly changing ocean conditions are resulting in changes in marine species and across entire ecosystems that, in turn, affect communities and individuals who rely on these resources for their livelihoods, culture, and sustenance. Marine social science, an emerging field that embraces diverse methods to understand human-ocean relationships, is increasingly called on to contribute to transdisciplinary ocean science that can inform the evidence-based policy and management needed to address these changes. Here, we review the state of marine social science as a growing field of study. First, we outline the history of marine social science, including the emergence of the field and the social science disciplines and community it encompasses. We then discuss current marine social science research themes as a framework to understand key ocean issues, which is followed by a commentary on the future of marine social science research.

Predictive Performance of Bayesian Stacking in Multilevel Education Data

The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the candidate model space over which averaging is taking place. Unlike Bayesian model averaging, the method of Bayesian stacking can account for model uncertainty without assuming that a true model exists. An issue with Bayesian stacking, however, is that it is an optimization technique that uses predictor-independent model weights and is, therefore, not fully Bayesian. Bayesian hierarchical stacking, proposed by Yao et al. further incorporates uncertainty by applying a hyperprior to the stacking weights. Considering the importance of multilevel models commonly applied in educational settings, this paper investigates via a simulation study and a real data example the predictive performance of original Bayesian stacking and Bayesian hierarchical stacking along with two other readily available weighting methods, pseudo-BMA and pseudo-BMA bootstrap (PBMA and PBMA+). Predictive performance is measured by the Kullback–Leibler divergence score. Although the differences in predictive performance among these four weighting methods in Bayesian stacking are small, we still find that Bayesian hierarchical stacking performs as well as conventional stacking, PBMA, and PBMA+ in settings where a true model is not assumed to exist.

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.

Which Skin Tone Measures Are the Most Inclusive? An Investigation of Skin Tone Measures for Artificial Intelligence

Skin tone plays a critical role in artificial intelligence (AI). However, many algorithms have exhibited unfair bias against people with darker skin tones. One reason this occurs is a poor understanding of how well the scales we use to measure and account for skin tone in AI actually represent the variation of skin tones in people affected by these systems. To address this, we conducted a survey with 2,214 people in the United States to compare three skin tone scales: The Fitzpatrick 6-point scale, Rihanna's Fenty Beauty 40-point skin tone palette, and a newly developed Monk 10-point scale from the social sciences. We find that the Fitzpatrick scale is perceived to be less inclusive than the Fenty and Monk skin tone scales, and this was especially true for people from historically marginalized communities (i.e., people with darker skin tones, BIPOCs, and women). We also find no statistically meaningful differences in perceived representation across the Monk skin tone scale and the Fenty Beauty palette. We discuss the ways in which our findings can advance the understanding of skin tone in both the social science and machine learning communities.

The ethics of engaged scholarship in a complex world

Abstract Social scientists must grapple with how to pursue knowledge about an uncertain and complex world. This challenge is accentuated when scholars wish to engage responsibly with policy-makers and the public in the interests of social betterment. In this article, we use the scholarly literature on uncertainty and complexity to examine how these issues complicate the practice of engaged scholarship. We ground our analysis in interviews with publicly engaged scholars on the ethical challenges they have faced and how they have navigated uncertainties and complexities in their applied work in peace and security. We identify four broad ethical dilemmas associated with publicly engaged scholarship and propose ways that scholars might begin to navigate these challenges. Our analysis urges greater acceptance of uncertainty and complexity in the social science community and associated epistemic humility in collective scholarship, pedagogy and public engagement.

Towards integration? Considering social aspects with large-scale computational models for nature-based solutions

Abstract Non-Technical Summary Information on social aspects of climate change intervention, such as behavioral choices and public acceptance, are often not included in global climate models. As a result, they have been critiqued for not adequately reflecting ‘real world’ conditions. At the same time, these models are important and influential policy tools. To improve these models, calls are being made for more interaction – or integration – between the social science and modelling research communities. Yet, it remains unclear how to achieve this. Responding to this gap, we explore what kind of integration is currently taking place, how, and opportunities for further development. Technical Summary The importance of social drivers of climate change interventions, or social aspects, is currently underrepresented in computational modelling projections. These parameters are largely excluded from estimates of technical mitigation potential, feasibility, and tools like integrated assessment models (IAMs) and other large-scale models that influence the development of climate policies and notable bodies like the Intergovernmental Panel on Climate Change. This paper contributes to calls being made within the research community to address this gap and strengthen linkages between modelling practices and social science insights. Using nature-based solutions (NbS) as a framing, we present the results of a critical literature review and interviews with multidisciplinary experts reflecting on the current state of integration around IAMs and opportunities to better capture social aspects within large-scale modelling processes. Our findings confirm the need to incorporate social aspects in IAMs, but highlight that how this happens in practice may depend on context, project objectives, or pragmatic choices rather than conceptual notions about what ‘good’ integration is. Nevertheless, some integration strategies are better than others, and concerns about data limitations and low capacity of the IAM community for engaging in integration can be overcome with sufficient support and complementary efforts from the broader research community. Social Media Summary Integrating social aspects in large-scale models requires complementary efforts from the broader research community.

Editorial: Launching Istanbul Policy Review

Welcome to the inaugural issue of the Istanbul Policy Review (IPR)! We are delighted to present a platform that serves as a dynamic bridge between social science communities across disciplines and continents. Our mission is to facilitate the sharing of valuable information, foster vibrant debates, and engage the widest possible audience. At the heart of the IPR lies a deep commitment to interdisciplinary and transdisciplinary work that challenges the status quo. We actively encourage researchers to explore innovative approaches and push the boundaries of current methodologies. Whether your work is applied or theoretical, we invite you to join us in shaping the future of social science research.

Multi-Layer Stochastic Block Interaction driven by Logistic Regression (MLSBI-LR) for Efficient Link Recommendation in Intra-Layer Linkage Graphs

Link Recommendation (LR) in complex networks has attracted huge interest in the social and computer science communities. Numerous networks, such as recommendation systems and social networks (which facilitate user contact), are probabilistic rather than deterministic due to the uncertainty surrounding the presence of links. Evaluating the various characteristics of such networks has frequently been tricky as the Intra-layer linkage graph requires at least two nodes to be in the same layer. Moreover, many existing LR methods mainly operate well on Single-Layer Graphs (SLGs) compared to Multi-Layer Graphs (MLGs) when nodes traverse multi-layers in a network of Intra-layer linkages. Considering this drawback, this paper proposes a Multi-Layer Stochastic Block Interaction method driven by Logistic Regression (MLSBI-LR) to exploit the bi-directional resources associated with Intra-layer linkages. Its inherent dependence on knowledge-based systems uses multi-criteria recommender systems to accommodate additional criteria and can modify neighborhood-based approaches. A multi-criteria network with relations over the same set of nodes is used since the modified neighbor-based method can exhibit rich dependence between entities and have available experimental data sets in MLGs to recommend links that would efficiently enrich users' experience. The accuracy and robustness of the proposed MLSBI-LR method compared to existing LR methods were extensively investigated using three distinct benchmark data sets and four evaluation metrics. Based on our experimental results across the databases and metrics, the proposed MLSBI-LR method performed significantly better (recording up to 17% increment in accuracy), recommending potential links in MLGs. Consequently, the proposed method may revolutionize link recommendation tasks in social networks by improving users' overall experience.

Will social scientists' never-ending disputes over words ever end?

Social science literatures are rife with disputes over their key words. I show that they tend to conflate two activities. One of them I call "word goes first!" It concerns word uses and how to appraise them. The other I call "distinction goes first!" It concerns distinctions and classifications and how to appraise them. For each activity, social science communities should get clear on what to do, what to circumvent, and what's at stake. I argue that both are practical reason activities. They should be partly driven by the common good, justice, and equality.

Inequalities and social sciences in neoliberal Indonesia

AbstractRising wealth and social inequalities around the world place great pressure on social researchers to interpret and explain the impact. However, it is equally important to recognise that scientists too have been part of the reproduction of social inequalities. This article expands on Burawoy's (2015) appeal to social scientists to acknowledge that social inequalities are not only external to the social science community specifically – and the scientific community more generally – but they also pervade academic labour and the way universities are managed and reformed. By taking the case of Indonesia, the largest economy in understudied Southeast Asia, this article reveals types of social inequalities reproduced and sustained through policies and practices within universities. These inequalities are the exclusionary effects of internationalisation, selective inclusion and corporatist bureaucracies, as well as regional inequalities in terms of infrastructure and capacity. We problematise the detrimental effects of marketisation in higher education on academic imagination and how it extends authoritarian developmentalism (1966–1998) to suit neoliberal demands. The article ends with propositions on how academics from the Global South can better understand their social position in an increasingly networked yet disconnected world skewed by multiple configurations of social inequalities.

International Studies and Struggles for Inclusion

Abstract In the 3 years between the 2019 and 2022 International Studies Association (ISA) meetings, the profound state of global economic, social, and political upheaval around the world has become unavoidably evident for much, if not most, of the world. Against the backdrop of the COVID-19 pandemic, movements for inclusion and resulting backlashes sprang up across the globe. As scholars of international affairs, the members of the ISA seek to understand and contextualize world events. However, our members and the organization itself are not passive observers of history. These struggles directly influence the personal experiences of many of our members, within and beyond our profession. For these reasons, ISA leadership felt that it was important to mark the 2022 meeting with a Sapphire Series panel to discuss ``International Studies and Struggles for Inclusion.'' The panel brought together ISA members from various sections and backgrounds to offer diverse perspectives on a host of topics: How does the field of international studies understand these developments? How do struggles for inclusion affect our members and community of social scientists? And perhaps most importantly, what should the ISA be doing about them, in terms of both scholarship and organizational decisions?

The Enlightenment of Max Weber's Sociological Thought to Chinese Educational Research

In the past 30 years, many scholars in the social science community in mainland China, especially in the field of sociology, have interpreted, discussed and expounded Weber's thoughts, social theory and its methodology, and have also produced many inspirations and influences on the field of education. Review Max Weber sociology thought of Chinese education research enlightenment course and key areas, is beneficial to understand Max Weber sociological thought spread in China, but also to understand the development of Chinese education theory and practice, for deep understanding Max Weber sociological thought is of great significance, and for the development of modern university provides new ideas and new material.

Socio-conversational systems: Three challenges at the crossroads of fields.

Socio-conversational systems are dialogue systems, including what are sometimes referred to as chatbots, vocal assistants, social robots, and embodied conversational agents, that are capable of interacting with humans in a way that treats both the specifically social nature of the interaction and the content of a task. The aim of this paper is twofold: 1) to uncover some places where the compartmentalized nature of research conducted around socio-conversational systems creates problems for the field as a whole, and 2) to propose a way to overcome this compartmentalization and thus strengthen the capabilities of socio-conversational systems by defining common challenges. Specifically, we examine research carried out by the signal processing, natural language processing and dialogue, machine/deep learning, social/affective computing and social sciences communities. We focus on three major challenges for the development of effective socio-conversational systems, and describe ways to tackle them.

The Gift that Keeps on Giving: Generosity is Contagious in Multiplayer Online Games

Understanding social interactions and generous behaviors have long been of considerable interest in the social sciences community. While the contagion of generosity is documented in the real world, less is known about such phenomenon in virtual worlds and whether it has an actionable impact on user behavior and retention. In this work, we analyze social dynamics in the virtual world of the popular massively multiplayer online role-playing game (MMORPG) Sky: Children of Light. We develop a framework to reveal the patterns of generosity in such social environments and provide empirical evidence of social contagion and contagious generosity. Players become more engaged in the game after playing with others and especially with friends. We also find that players who experience generosity first-hand or even observe other players conduct generous acts become more generous themselves in the future. Additionally, we show that both receiving and observing generosity lead to higher future engagement in the game. Since Sky resembles the real world from a social play aspect, the implications of our findings also go beyond this virtual world.

Gábor Kézdi Memorial Conference – Conference Report

The Hungarian Economic Association organised a conference in memory of Gábor Kézdi on 20 December 2021. Gábor Kézdi, one of the most influential Hungarian economists of his generation, died last year at the age of 50. His career as a researcher and teacher was an inspiration and an example not only to the economics profession but also to the wider social science community (including the author of these lines). His research addressed many topics of interest to sociologists and social policy researchers, such as educational inequalities, school segregation, prejudice and discrimination, the situation of the Roma population in Hungary and empirical studies of the effects of social policies.

The missing risks of climate change.

The risks of climate change are enormous, threatening the lives and livelihoods of millions to billions of people. The economic consequences of many of the complex risks associated with climate change cannot, however, currently be quantified. Here we argue that these unquantified, poorly understood and often deeply uncertain risks can and should be included in economic evaluations and decision-making processes. We present an overview of these unquantified risks and an ontology of them founded on the reasons behind their lack of robust evaluation. These consist of risks missing owing to delays in sharing knowledge and expertise across disciplines, spatial and temporal variations of climate impacts, feedbacks and interactions between risks, deep uncertainty in our knowledge, and currently unidentified risks. We highlight collaboration needs within and between the natural and social science communities to address these gaps. We also provide an approach for integrating assessments or speculations of these risks in a way that accounts for interdependencies, avoids double counting and makes assumptions clear. Multiple paths exist for engaging with these missing risks, with both model-based quantification and non-model-based qualitative assessments playing crucial roles. A wide range of climate impacts are understudied or challenging to quantify, and are missing from current evaluations of the climate risks to lives and livelihoods. Strong interdisciplinary collaboration and deeper engagement with uncertainty is needed to properly inform policymakers and the public about climate risks.

Explanation sets: A general framework for machine learning explainability

Explainable Machine Learning (ML) is an emerging field of Artificial Intelligence that has gained popularity in the last decade. It focuses on explaining ML models and their predictions, enabling people to understand the rationale behind them. Counterfactuals and semifactuals are two instances of Explainable ML techniques that explain model predictions using other observations. These techniques are based on the comparison between the observation to be explained and another one. In counterfactuals, their prediction is different, and in semifactuals, it is the same. Both techniques have been studied in the Social Sciences and Explainable ML communities, and they have different use cases and properties. In this paper, the Explanation Set framework, an approach that unifies counterfactuals and semifactuals, is introduced. Explanation Sets are example-based explanations defined in a neighborhood where most observations satisfy a grouping measure. The neighborhood allows defining and combining restrictions. The grouping measure determines if the explanations are counterfactuals (dissimilarity) or semifactuals (similarity). Besides providing a unified framework, the major strength of the proposal is to extend these explanations to other tasks such as regression by using an appropriate grouping measure. The proposal is validated in a regression and classification task using several neighborhoods and grouping measures.

Identifying spreading influence nodes for social networks

The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.