Hasil untuk "Sociology"

B. Bernstein

J. Moody

Chiara Amato, Mario Cerasoli

The article explores the revitalization of Italy's "inner areas" - —fragile territories facing challenges such as depopulation, abandonment, and a lack of essential services, primarily due to limited mobility. The enhancement of secondary and disused railways is proposed as a tool for territorial rebalancing, to be achieved through multilevel governance that integrates spatial planning, interinstitutional cooperation, and the strategic allocation of financial and economic resources. This approach aims to move beyond sectoral perspectives on infrastructure networks.  The Axis Contract is introduced as an integrated framework linking urban planning and mobility, centered on the right to mobility and the empowerment of local communities.  The article is structured into three parts: the first examines the relationship between territory, mobility, and infrastructure policies; the second analyzes the French Contrat d’axe model and its applicability in Italy; and the third presents the results of an interdisciplinary study on the reactivation of the Civitavecchia -Capranica - Orte railway line.  The findings confirm that the Axis Contract is an effective tool for integrating urban and mobility planning, addressing accessibility needs, and promoting the sustainable rebalancing of territories.

Paolo Boldi, Flavio Furia, Chiara Prezioso

Centrality indices are used to rank the nodes of a graph by importance: this is a common need in many concrete situations (social networks, citation networks, web graphs, for instance) and it was discussed many times in sociology, psychology, mathematics and computer science, giving rise to a whole zoo of definitions of centrality. Although they differ widely in nature, many centrality measures are based on shortest-path distances: such centralities are often referred to as geometric. Geometric centralities can use the shortest-path-length information in many different ways, but most of the existing geometric centralities can be defined as a linear transformation of the distance-count vector (that is, the vector containing, for every index t, the number of nodes at distance t). In this paper we study this class of centralities, that we call linear (geometric) centralities, in their full generality. In particular, we look at them in the light of the axiomatic approach, and we study their expressivity: we show to what extent linear centralities can be used to distinguish between nodes in a graph, and how many different rankings of nodes can be induced by linear centralities on a given graph. The latter problem (which has a number of possible applications, especially in an adversarial setting) is solved by means of a linear programming formulation, which is based on Farkas' lemma, and is interesting in its own right.

Runshi Tang, Runbing Zheng, Anru R. Zhang et al.

Dynamic multilayer networks are frequently used to describe the structure and temporal evolution of multiple relationships among common entities, with applications in fields such as sociology, economics, and neuroscience. However, exploration of analytical methods for these complex data structures remains limited. We propose a functional tensor-based model for dynamic multilayer networks, with the key feature of capturing the shared structure among common vertices across all layers, while simultaneously accommodating smoothly varying temporal dynamics and layer-specific heterogeneity. The proposed model and its embeddings can be applied to various downstream network inference tasks, including dimensionality reduction, vertex community detection, analysis of network evolution periodicity, visualization of dynamic network evolution patterns, and evaluation of inter-layer similarity. We provide an estimation algorithm based on functional tensor Tucker decomposition and the reproducing kernel Hilbert space framework, with an effective initialization strategy to improve computational efficiency. The estimation procedure can be extended to address more generalized functional tensor problems, as well as to handle missing data or unaligned observations. We validate our method on simulated data and two real-world cases: the dynamic Citi Bike trip network and an international food trade dynamic multilayer network, with each layer corresponding to a different product.

Junhao Yu, Yan Zhuang, YuXuan Sun et al.

Accurately assessing internal human states is key to understanding preferences, offering personalized services, and identifying challenges in real-world applications. Originating from psychometrics, adaptive testing has become the mainstream method for human measurement and has now been widely applied in education, healthcare, sports, and sociology. It customizes assessments by selecting the fewest test questions . However, current adaptive testing methods face several challenges. The mechanized nature of most algorithms leads to guessing behavior and difficulties with open-ended questions. Additionally, subjective assessments suffer from noisy response data and coarse-grained test outputs, further limiting their effectiveness. To move closer to an ideal adaptive testing process, we propose TestAgent, a large language model (LLM)-powered agent designed to enhance adaptive testing through interactive engagement. This is the first application of LLMs in adaptive testing. TestAgent supports personalized question selection, captures test-takers' responses and anomalies, and provides precise outcomes through dynamic, conversational interactions. Experiments on psychological, educational, and lifestyle assessments show our approach achieves more accurate results with 20% fewer questions than state-of-the-art baselines, and testers preferred it in speed, smoothness, and other dimensions.

Zhiqin Ma, Chunhua Zeng, Yi-Cheng Zhang et al.

Complex systems can undergo critical transitions, where slowly changing environmental conditions trigger a sudden shift to a new, potentially catastrophic state. Early warning signals for these events are crucial for decision-making in fields such as ecology, biology and climate science. Generic early warning signals motivated by dynamical systems theory have had mixed success on real noisy data. More recent studies found that deep learning classifiers trained on synthetic data could improve performance. However, neither of these methods take advantage of historical, system-specific data. Here, we introduce an approach that trains machine learning classifiers directly on surrogate data of past transitions, namely surrogate data-based machine learning (SDML). The approach provides early warning signals in empirical and experimental data from geology, climatology, sociology, and cardiology with higher sensitivity and specificity than two widely used generic early warning signals -- variance and lag-1 autocorrelation. Since the approach is trained directly on surrogates of historical data, it is not bound by the restricting assumption of a local bifurcation like previous methods. This system-specific approach can contribute to improved early warning signals to help humans better prepare for or avoid undesirable critical transitions.

T. Snelleman, B. M. Renting, H. H. Hoos et al.

Graph-structured data naturally occurs in many research fields, such as chemistry and sociology. The relational information contained therein can be leveraged to statistically model graph properties through geometrical deep learning. Graph neural networks employ techniques, such as message-passing layers, to propagate local features through a graph. However, message-passing layers can be computationally expensive when dealing with large and sparse graphs. Graph pooling operators offer the possibility of removing or merging nodes in such graphs, thus lowering computational costs. However, pooling operators that remove nodes cause data loss, and pooling operators that merge nodes are often computationally expensive. We propose a pooling operator that merges nodes so as not to cause data loss but is also conceptually simple and computationally inexpensive. We empirically demonstrate that the proposed pooling operator performs statistically significantly better than edge pool on four popular benchmark datasets while reducing time complexity and the number of trainable parameters by 70.6% on average. Compared to another maximally powerful method named Graph Isomporhic Network, we show that we outperform them on two popular benchmark datasets while reducing the number of learnable parameters on average by 60.9%.

Kishankumar Bhimani, Khushbu Saradva

This research study explores the new dynamics of employee-organi-zation relationships (EOR) [6] using advanced data science methodologies and presents findings through accessible visualizations. Leveraging a dataset pro-cured from a comprehensive nationwide big employee survey, this study employs innovative strategy for theoretical researcher by using our state-of-the-art visual-ization. The results present insightful visualizations encapsulating demographic analysis, workforce satisfaction, work environment scrutiny, and the employee's view via word cloud interpretations and burnout predictions. The study underscores the profound implications of data science across various management sectors, enhancing understanding of workplace dynamics and pro-moting mutual growth and satisfaction. This multifaceted approach caters to a diverse array of readers, from researchers in sociology and management to firms seeking detailed understanding of their workforce's satisfaction, emphasizing on practicality and interpretability. The research encourages proactive measures to improve workplace environ-ments, boost employee satisfaction, and foster healthier, more productive organ-izations. It serves as a resourceful tool for those committed to these objectives, manifesting the transformative potential of data science in driving insightful nar-ratives about workplace dynamics and employee-organization relationships. In essence, this research unearths valuable insights to aid management, HR profes-sionals, and companies

Edgar De Jesus Ramos Muñoz, Veronica Ann Swanson, Christopher Johnson et al.

Persevering with home rehabilitation exercise is a struggle for millions of people in the US each year. A key factor that may influence motivation to engage with rehabilitation exercise is the challenge level of the assigned exercises, but this hypothesis is currently supported only by subjective, self-report. Here, we studied the relationship between challenge level and perseverance using long-term, self-determined exercise patterns of a large number of individuals (N = 2,581) engaging in home rehabilitation with a sensor-based exercise system without formal supervision. FitMi is comprised of two puck-like sensors and a library of 40 gamified exercises for the hands, arms, trunk, and legs that are designed for people recovering from a stroke. We found that individuals showed the greatest perseverance with the system over a 2-month period if they had (1) a moderate level of motor impairment and (2) high but not perfect success during the 1st week at completing the exercise game. Further, a steady usage pattern (vs. accelerating or decelerating use) was associated with more overall exercise, and declines in exercise amount over time were associated with exponentially declining session initiation probability rather than decreasing amounts of exercise once a session was initiated. These findings confirm that an optimized challenge level and regular initiation of exercise sessions predict achievement of a greater amount of overall rehabilitation exercise in a group of users of commercial home rehabilitation technology and suggest how home rehabilitation programs and exercise technologies can be optimized to promote perseverance.

E. A. Evdokimova, A. Yu. Fomin, M. S. Yumatov

Currently, the external conditions for the functioning of industrial enterprises are formed in such a way that the achievement of their goals is seen impossible without the implementation of innovative activities. Due to the high cost of innovations, as well as the need to conduct these processes on an ongoing basis, industrial enterprises lack their own funds to finance innovation activities. The state is interested in the innovative development of industrial enterprises. This is one of the reasons why it participates in the formation of their funding sources. The article substantiates the need for state support, as well as analyzes the options for its provision by type of financing sources. For each type of sources, options for providing large-scale support, implying a large coverage area, as well as point-directed support, are also highlighted.

Chai K. Toh

Abstract Japan has long embarked on the city transformation journey, from green city to eco city, ubiquitous city, sustainable city, and now zero‐emission city. A smart city is not considered smart if it is not green, not sustainable, and not environmentally friendly. The journey is long but progressive, and the Japanese government has been supportive in its city transformational efforts. Japanese cities are marked by distinctive local cultures, habitat, people, food, beliefs, history etc. From Tokyo to Osaka, Nagoya, Fukuoka, Kobe and Yokohama, cities in Japan have advanced into high levels of urbanisation. The increase in population and traffic utilisation have resulted in higher energy demands and pollution of the environment. These concerns have motivated Japan to strive for cities with zero‐emission and Tokyo, as the country's capital, will lead in this drive. The author outlines the current situation in Japan, the impact of global warming and climate change, presents the motivation behind the new strategy, and narrates and discusses Tokyo city’s zero‐emission strategy and execution plans and how it works towards achieving net zero by 2050.

N. Yu. Kaufman, S. Yu. Zelentsova

The article explores the principles of building a competitive specialist in adigitally transformed economy, and presents the prerequisites that justify the development of staff training through the automation of HR technologies. The interpretation of the concepts «digital economy», «EdTech», «staff training», «gamification» is given. The main barriers preventing the implementation of HR Tech solutions in organisations are showed. The use of statistical analysis and comparison methods made it possible to present the areas of HR Tech, where digitalisation most actively improves and automates processes. It has been revealed that the main drivers of efficiency in the HR sphere can be modern tools and platforms that contribute to the introduction of automation in the personnel training system. The events of recent years have led to the understanding that it will no longer be possible to work effectively without the use of new technologies, therefore, the advantages of automating learning processes are highlighted. The role and area of responsibility of the training and development manager in the process of staff training has been established. Scenarios for solving the problem of a shortage of a digital specialist have been determined.

J. Brandon Carter, Christopher R. Browning, Bethany Boettner et al.

Collective efficacy -- the capacity of communities to exert social control toward the realization of their shared goals -- is a foundational concept in the urban sociology and neighborhood effects literature. Traditionally, empirical studies of collective efficacy use large sample surveys to estimate collective efficacy of different neighborhoods within an urban setting. Such studies have demonstrated an association between collective efficacy and local variation in community violence, educational achievement, and health. Unlike traditional collective efficacy measurement strategies, the Adolescent Health and Development in Context (AHDC) Study implemented a new approach, obtaining spatially-referenced, place-based ratings of collective efficacy from a representative sample of individuals residing in Columbus, OH. In this paper, we introduce a novel nonstationary spatial model for interpolation of the AHDC collective efficacy ratings across the study area which leverages administrative data on land use. Our constructive model specification strategy involves dimension expansion of a latent spatial process and the use of a filter defined by the land-use partition of the study region to connect the latent multivariate spatial process to the observed ordinal ratings of collective efficacy. Careful consideration is given to the issues of parameter identifiability, computational efficiency of an MCMC algorithm for model fitting, and fine-scale spatial prediction of collective efficacy.

Pierre Canisius Kamanzi, Gaële Goastellec, Laurence Pelletier

The objective of this article is to revisit the role of public policies in the social production and reproduction of university access inequalities that have been made evident more than ever in the current intensified mass higher education context. Although the situation is complex and varies from one societal context to another, a systematic review of the existing literature highlights the undeniable responsibility of public policies in this reproduction through three main channels: guidance systems and educational pathways, institutions’ stratification and hierarchization of fields of study and, finally, the financing of studies and tuition fees.

Shaolun Ruan, Yong Wang, Qiang Guan

State change comparison of multiple data items is often necessary in multiple application domains, such as medical science, financial engineering, sociology, biological science, etc. Slope graphs and grouped bar charts have been widely used to show a "before-and-after" story of different data states and indicate their changes. However, they visualize state changes as either slope or difference of bars, which has been proved less effective for quantitative comparison. Also, both visual designs suffer from visual clutter issues with an increasing number of data items. In this paper, we propose Intercept Graph, a novel visual design to facilitate effective interactive comparison of state changes. Specifically, a radial design is proposed to visualize the starting and ending states of each data item and the line segment length explicitly encodes the "state change". By interactively adjusting the radius of the inner circular axis, Intercept Graph can smoothly filter the large state changes and magnify the difference between similar state changes, mitigating the visual clutter issues and enhancing the effective comparison of state changes. We conducted a case study through comparing Intercept Graph with slope graphs and grouped bar charts on real datasets to demonstrate the effectiveness of Intercept Graph.

Jaehyuk Park, Bogdan State, Monica Bhole et al.

Due to their essential role as places for socialization, "third places" - social places where people casually visit and communicate with friends and neighbors - have been studied by a wide range of fields including network science, sociology, geography, urban planning, and regional studies. However, the lack of a large-scale census on third places kept researchers from systematic investigations. Here we provide a systematic nationwide investigation of third places and their social networks, by using Facebook pages. Our analysis reveals a large degree of geographic heterogeneity in the distribution of the types of third places, which is highly correlated with baseline demographics and county characteristics. Certain types of pages like "Places of Worship" demonstrate a large degree of clustering suggesting community preference or potential complementarities to concentration. We also found that the social networks of different types of social place differ in important ways: The social networks of 'Restaurants' and 'Indoor Recreation' pages are more likely to be tight-knit communities of pre-existing friendships whereas 'Places of Worship' and 'Community Amenities' page categories are more likely to bridge new friendship ties. We believe that this study can serve as an important milestone for future studies on the systematic comparative study of social spaces and their social relationships.

Michele Coscia

Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more. This book aims at providing the first access to all these tools. It is intended as an "Atlas", because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist.

Riccardo Emilio Chesta

This introductory essay puts the sociological debates on FIAT in perspective, by showing the relations between the evolution of the Italian multinational and the changes in the practice of sociological inquiry. At several junctures, the debate on FIAT has been so publicly relevant that it became a difficult and somehow hostile terrain for the development of a “quiet and cold” explanatory sociology independent of any positioning in the field of contention. But this is exactly what makes the relation between the Italian automobile factory and sociology a specific one, a privileged perspective through which it is possible to look not only at the changes in the world of labor, but also at the evolution of the discipline over time.

Harsh Gupta, Mason A. Porter

The study of network formation is pervasive in economics, sociology, and many other fields. In this paper, we model network formation as a `choice' that is made by nodes in a network to connect to other nodes. We study these `choices' using discrete-choice models, in which an agent chooses between two or more discrete alternatives. We employ the `repeated-choice' (RC) model to study network formation. We argue that the RC model overcomes important limitations of the multinomial logit (MNL) model, which gives one framework for studying network formation, and that it is well-suited to study network formation. We also illustrate how to use the RC model to accurately study network formation using both synthetic and real-world networks. Using edge-independent synthetic networks, we also compare the performance of the MNL model and the RC model. We find that the RC model estimates the data-generation process of our synthetic networks more accurately than the MNL model. In a patent citation network, which forms sequentially, we present a case study of a qualitatively interesting scenario -- the fact that new patents are more likely to cite older, more cited, and similar patents -- for which employing the RC model yields interesting insights.