Hasil untuk "Sociology"

S. Lyng

B. Wellman

K. Popper

VOLUME 1: THE SPELL OF PLATO Introduction, The Spell of Plato, THE MYTH OF ORIGIN AND DESTINY 1. Historicism and the Myth of Destiny 2. Heraclitus 3. Plato's Theory of Forms or Ideas PLATO'S DESCRIPTIVE SOCIOLOGY: 4. Change and Rest 5. Nature and Convention, PLATO'S POLITICAL PROGRAMME: 6. Totalitarian Justice 7. The Principle of Leadership 8. The Philosopher King 9. Aestheticism, Perfectionism, Utopianism THE BACKGROUND OF PLATO'S ATTACK: 10. The Open Society and its Enemies Notes Addenda Index of Platonic Passages Index of Names Index of Subjects VOLUME 2: THE HIGH TIDE OF PROPHECY The Rise of Oracular Philosophy 11. The Aristotelian Roots of Hegelianism 12. Hegel and The New Tribalism MARX'S METHOD: 13. Sociological Determinism 14. The Autonomy of Sociology 15. Economic Historicism 16. The Classes 17. The Legal and the Social System MARX'S PROPHECY: 18. The Coming of Socialism 19. The Social Revolution 20. Capitalism and Its Fate 21. An Evaluation MARX'S ETHICS: 22. The Moral Theory of Historicism THE AFTERMATH: 23. The Sociology of Knowledge 24. Oracular Philosophy and the Revolt against Reason CONCLUSION: 25. Has History any aning? Notes Addenda (1961, 1965) Index of Names Index of Subjects

B. Hughes, K. Paterson

C. Barnes, G. Mercer, T. Shakespeare

J. Prosser

Jeffrey Alexander

I. Røpke

Fernando Diaz-Diaz, Elena Candellone, Miguel A. Gonzalez-Casado et al.

Signed networks provide a principled framework for representing systems in which interactions are not merely present or absent but qualitatively distinct: friendly or antagonistic, supportive or conflicting, excitatory or inhibitory. This polarity reshapes how we think about structure and dynamics in complex systems: a negative tie is not simply a missing positive one but a constraint that generates tension, and possibly asymmetry. Across disciplines, from sociology to neuroscience and machine learning, signed networks provide a shared language to formalise duality, balance, and opposition as integral components of system behaviour. This review provides a comprehensive and foundational summary of signed network theory. It formalises the mathematical principles of signed graphs and surveys signed-network-specific measures, including signed degree distributions, clustering, centralities, motifs, and Laplacians. It revisits balance theory, tracing its cognitive and structural formulations and their connections to frustration. Structural aspects of signed networks are examined, analysing key topics such as null models, node embeddings, sign prediction, and community detection. Subsequent sections address dynamical processes on and of signed networks, such as opinion dynamics, contagion models, and data-driven approaches for studying evolving networks. Practical challenges in constructing, inferring and validating signed data from real-world systems are also highlighted, and we offer an overview of currently available datasets. We also address common pitfalls and challenges that arise when modelling or analysing signed data. Overall, this review integrates theoretical foundations, methodological approaches, and cross-domain examples, providing a structured entry point and a reference framework for researchers interested in the study of signed networks in complex systems.

Gian Maria Campedelli

While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized by a society with a growing presence of autonomous machines is high. Autonomous AI agents are already deployed and active across several industries and digital environments and alongside human-human and human-machine interactions, machine-machine interactions are poised to become increasingly prevalent. Given these developments, I argue that criminology must begin to address the implications of this transition for crime and social control. Drawing on Actor-Network Theory and Woolgar's decades-old call for a sociology of machines -- frameworks that acquire renewed relevance with the rise of generative AI agents -- I contend that criminologists should move beyond conceiving AI solely as a tool. Instead, AI agents should be recognized as entities with agency encompassing computational, social, and legal dimensions. Building on the literature on AI safety, I thus examine the risks associated with the rise of multi-agent AI systems, proposing a dual taxonomy to characterize the channels through which interactions among AI agents may generate deviant, unlawful, or criminal outcomes. I then advance and discuss four key questions that warrant theoretical and empirical attention: (1) Can we assume that machines will simply mimic humans? (2) Will crime theories developed for humans suffice to explain deviant or criminal behaviors emerging from interactions between autonomous AI agents? (3) What types of criminal behaviors will be affected first? (4) How might this unprecedented societal shift impact policing? These questions underscore the urgent need for criminologists to theoretically and empirically engage with the implications of multi-agent AI systems for the study of crime and play a more active role in debates on AI safety and governance.

Ottavio Khalifa, Viet-Thi Tran, Alan Balendran et al.

Objective: To provide an overview of clustering methods for categorical time series (CTS), a data structure commonly found in epidemiology, sociology, biology, and marketing, and to support method selection in regards to data characteristics. Methods: We searched PubMed, Web of Science, and Google Scholar, from inception up to November 2024 to identify articles that propose and evaluate clustering techniques for CTS. Methods were classified according to three major families -- distance-based, feature-based, and model-based -- and assessed on their ability to handle data challenges such as variable sequence length, multivariate data, continuous time, missing data, time-invariant covariates, and large data volumes. Results: Out of 14607 studies, we included 124 articles describing 129 methods, spanning domains such as artificial intelligence, social sciences, and epidemiology. Distance-based methods, particularly those using Optimal Matching, were most prevalent, with 56 methods. We identified 28 model-based methods, which demonstrated superior flexibility for handling complex data structures such as multivariate data, continuous time and time-invariant covariates. We also recorded 45 feature-based approaches, which were on average more scalable but less flexible. A searchable Web application was developed to facilitate method selection based on dataset characteristics ( https://cts-clustering-scoping-review-7sxqj3sameqvmwkvnzfynz.streamlit.app/ ) Discussion: While distance-based methods dominate, model-based approaches offer the richest modeling potential but are less scalable. Feature-based methods favor performance over flexibility, with limited support for complex data structures. Conclusion: This review highlights methodological diversity and gaps in CTS clustering. The proposed typology aims to guide researchers in selecting methods for their specific use cases.

Giorgio Tripodi, Xiang Zheng, Yifan Qian et al.

Tenure is a cornerstone of the US academic system, yet its relationship to faculty research trajectories remains poorly understood. Conceptually, tenure systems may act as a selection mechanism, screening in high-output researchers; a dynamic incentive mechanism, encouraging high output prior to tenure but low output after tenure; and a creative search mechanism, encouraging tenured individuals to undertake high-risk work. Here, we integrate data from seven different sources to trace US tenure-line faculty and their research outputs at an unprecedented scale and scope, covering over 12,000 researchers across 15 disciplines. Our analysis reveals that faculty publication rates typically increase sharply during the tenure track and peak just before obtaining tenure. Post-tenure trends, however, vary across disciplines: in lab-based fields, such as biology and chemistry, research output typically remains high post-tenure, whereas in non-lab-based fields, such as mathematics and sociology, research output typically declines substantially post-tenure. Turning to creative search, faculty increasingly produce novel, high-risk research after securing tenure. However, this shift toward novelty and risk-taking comes with a decline in impact, with post-tenure research yielding fewer highly cited papers. Comparing outcomes across common career ages but different tenure years or comparing research trajectories in tenure-based and non-tenure-based research settings underscores that breaks in the research trajectories are sharply tied to the individual's tenure year. Overall, these findings provide a new empirical basis for understanding the tenure system, individual research trajectories, and the shape of scientific output.

Maxence Mautray

L’émergence, depuis près d’une décennie, d’enjeux écologiques associés à la réduction des déchets, entraîne la mise en place de politiques locales incitatives par les collectivités en responsabilité de la collecte et du traitement des déchets ménagers. Ces politiques visent à écologiser la gestion des déchets des ménages, par l’usage d’instruments d’action publique de diverses natures : informationnels, tarifaires et techniques. L’étude sociologique, par entretiens semi-directifs menés auprès des habitants, de la mise en place d’une telle politique dans un territoire rural et précaire permet de mettre en lumière une réception contestée de la part de la population. Les résistances sont de natures multiples : tout d’abord, le service public des déchets n’est pas perçu comme un acteur dont les injonctions au changement de comportement au quotidien sont légitimes, car sa probité est remise en cause dans le même temps par l’organisation et l’efficacité de l’industrie du recyclage. Ensuite, la tarification incitative est largement perçue comme une facture supplémentaire injuste, car ce financement ne comporte pas de logique redistributive. Enfin, la mise en place de l’apport volontaire de tous les flux de déchets est largement vécue comme le retrait d’un des derniers services de proximité en zone rurale. Bien que l’on puisse croire que ce sentiment de rejet se focalise contre l’écologisation de l’action publique des déchets, il est bien plus orienté en fait vers la réorganisation de la collectivité territoriale étudiée, démontrant la centralité des questions de communication et de fiscalité du service public dans les réponses politiques à la crise écologique.

Yoshua Bengio, Prateek Gupta, Lu Li et al.

The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.

Sijing Tu, Stefan Neumann, Aristides Gionis

In recent years, online social networks have been the target of adversaries who seek to introduce discord into societies, to undermine democracies and to destabilize communities. Often the goal is not to favor a certain side of a conflict but to increase disagreement and polarization. To get a mathematical understanding of such attacks, researchers use opinion-formation models from sociology, such as the Friedkin--Johnsen model, and formally study how much discord the adversary can produce when altering the opinions for only a small set of users. In this line of work, it is commonly assumed that the adversary has full knowledge about the network topology and the opinions of all users. However, the latter assumption is often unrealistic in practice, where user opinions are not available or simply difficult to estimate accurately. To address this concern, we raise the following question: Can an attacker sow discord in a social network, even when only the network topology is known? We answer this question affirmatively. We present approximation algorithms for detecting a small set of users who are highly influential for the disagreement and polarization in the network. We show that when the adversary radicalizes these users and if the initial disagreement/polarization in the network is not very high, then our method gives a constant-factor approximation on the setting when the user opinions are known. To find the set of influential users, we provide a novel approximation algorithm for a variant of MaxCut in graphs with positive and negative edge weights. We experimentally evaluate our methods, which have access only to the network topology, and we find that they have similar performance as methods that have access to the network topology and all user opinions. We further present an NP-hardness proof, which was an open question by Chen and Racz [IEEE Trans. Netw. Sci. Eng., 2021].

V. D. Melnikova, L. A. Ulianitckaia

Introduction. The relevance of the study is explained by the necessity to preserve Frenchspeaking culture and language on the territory of Canada in the conditions of globalization, the spread of American mass culture and the significant impact of English as a global language. The purpose of the work is to describe and analyze the sociolinguistic particularities of the existence of the French language in the English-speaking competitive environment in Canada.Methodology and sources. During the study, the following sociolinguistic methods were used: descriptive method, comparison method, continuous sampling method, sociolinguistic analysis method, quantitative data processing method, questionnaire survey. The research is based on the material of Сanadian media, legislative acts regulating the state's language policy, data obtained through surveys of Canadian citizens, and 5,234 inscriptions in five Canadian cities which were selected to examine the country's linguistic landscape.Results and discussion. Under Canadian laws, French and English have equal status in parliamentary readings, in public services and in everyday life, as well as in education, radio and television. However, English is the dominant language and has great prestige throughout Canada. All of the evidence collected shows the decisive superiority of English as the primary means of communication in media, advertising and politics. Existing legislation to protect and promote the use of both official languages is not fully enforced, as evidenced by numerous complaints from Canadians to the Commissioner of Official Languages.Conclusion. In Canada, a multi-component exoglossic linguistic situation has developed with two official languages – English and French, which is characterized by the existence of natural bilingualism. Linguistic minorities, such as Anglophones in Quebec and Francophones outside Quebec, may experience linguistic discrimination, creating tensions between residents of the same country. French is significantly influenced by the majority English and immigrant languages that dominate the linguistic landscape of Canadian cities.

Stephen Bailey, Christian Bierlich, Andy Buckley et al.

We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-term future reuse, in order to maximise the scientific impact of publicly funded particle-physics experiments. We cover the needs of both the experimental and theoretical particle physics communities, and outline the goals and benefits that are uniquely enabled by analysis recasting and reinterpretation. We also discuss technical challenges and infrastructure needs, as well as sociological challenges and changes, and give summary recommendations to the particle-physics community.

Alina Schmitz, Claudius Garten, Simon Kühne et al.

Abstract Background This study investigates individual and regional determinants of worries about inadequate medical treatment in case of a COVID-19 infection, an important indicator of mental wellbeing in pandemic times as it potentially affects the compliance with mitigation measures and the willingness to get vaccinated. The analyses shed light on the following questions: Are there social inequalities in worries about inadequate medical treatment in case of a COVID-19 infection? What is the role of the regional spread of COVID-19 infections and regional healthcare capacities? Methods Based on data derived from the German Socioeconomic Panel (SOEP), a representative sample of the German population aged 18 years and over, we estimated multilevel logistic regression models with individual-level (level 1) and regional-level (level 2) variables. The regional variables of interest were (a) the number of COVID-19 infections, (b) the number of hospital beds as an overall measure of the regional healthcare capacities, and (c) the number of free intensive care units as a measure of the actual capacities for treating patients with severe courses of COVID-19. Results Women, older respondents, persons with migrant background and those with a lower socioeconomic status were more likely to report worries about inadequate medical treatment in case of a COVID-19 infection. Moreover, respondents with chronic illness, lower subjective health and those who consider COVID-19 as a threat for their own health were more likely to report worries. In addition, also regional characteristics were relevant. Worries were more common in poorer regions with higher COVID-19 infections and worse health infrastructure as indicated by the number of hospital beds. Conclusions The analysis not only indicates that several social groups are more concerned about inadequate medical treatment in case of a COVID-19 infection, but also highlights the need for considering regional-level influences, such as the spread of the virus, poverty rates and healthcare infrastructure, when analyzing the social and health-related consequences of the pandemic.

Junchen Jin, Mark Heimann, Di Jin et al.

While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles--i.e., similar functions, ties or interactions with nodes in other positions--irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small synthetic or real networks with labels that are not perfectly defined, and its connection to sociological equivalences has hitherto been vague and tenuous. With new node embedding methods being developed at a breakneck pace, proper evaluation and systematic characterization of existing approaches will be essential to progress. To fill in this gap, we set out to understand what types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure, classifier, label definitions). We find that degree distributions within nodes' local neighborhoods can lead to simple yet effective baselines in their own right and guide the future development of structural embedding. We hope that our findings can influence the design of further node embedding methods and also pave the way for more comprehensive and fair evaluation of structural embedding methods.

David Frank, Suzan M. Walters

Background: Often people assume that entry into drug treatment is a voluntary action for persons who use drugs (PWUD). This narrative informs the organizational and regulatory structure of most treatment programs and consequently affects patients’ ability to exert agency over their own treatment. Yet, this view ignores the complex interplay between individual and structural factors in peoples’ decision-making processes, particularly among people who use drugs who are stigmatized and criminalized. Treatment programs that assume voluntary entry may lack appropriate services for the populations of treatment seekers that they serve.Methods: This paper uses semi-structured interviews with 42 participants in Opioid Substitution Treatment (OST) (including patients, clinic doctors and staff, and advocates) informed by one of the author’s own lived experience in OST, to examine patients’ treatment decisions, and in particular, if and how, the structural context of drugs’ illegality/criminalization affected their willingness to pursue treatment. A Critical Discourse Analysis was used to identify key themes.Results: Interview data demonstrates that most people who use drugs enter treatment under constrained conditions related to drugs’ illegality. Themes that emerged included: 1. A feeling of limited choices due to drugs’ illegality; 2. Peer and family pressure; 3. Fear of losing children; and 4. Internalized stigma (i.e. feeling they are dirty or bad for using).Conclusion: Narratives that frame PWUD’s treatment decisions as volitional provide political cover to policies that criminalize PWUD by obscuring their effect on PWUD’s treatment decisions. Treatment models, particularly those that serve highly criminalized populations, should be re-conceptualized outside of normative narratives of individual choice, and be broadened to understand how larger structures constrain choices. By looking at macro-level factors, including the interplay of criminalization and drug treatment, programs can begin to understand the complexity of PWUD motivations to enter drug treatment. Recognizing the role of the War on Drugs as a force of oppression for people who use drugs, and that their treatment decisions are made within that setting, may enable people in treatment, and providers, to develop more productive ways of interacting with one another. Additionally, this may lead to better retention in treatment programs.