Hasil untuk "Sociology"

M. Kelly, M. Barker

OBJECTIVE To demonstrate that six common errors made in attempts to change behaviour have prevented the implementation of the scientific evidence base derived from psychology and sociology; to suggest a new approach which incorporates recent developments in the behavioural sciences. STUDY DESIGN The role of health behaviours in the origin of the current epidemic of non-communicable disease is observed to have driven attempts to change behaviour. It is noted that most efforts to change health behaviours have had limited success. This paper suggests that in policy-making, discussions about behaviour change are subject to six common errors and that these errors have made the business of health-related behaviour change much more difficult than it needs to be. METHODS Overview of policy and practice attempts to change health-related behaviour. RESULTS The reasons why knowledge and learning about behaviour have made so little progress in alcohol, dietary and physical inactivity-related disease prevention are considered, and an alternative way of thinking about the behaviours involved is suggested. This model harnesses recent developments in the behavioural sciences. CONCLUSION It is important to understand the conditions preceding behaviour psychologically and sociologically and to combine psychological ideas about the automatic and reflective systems with sociological ideas about social practice.

John R. Kelly

Ezra W. Zuckerman

So Kubota, Hiromu Yakura, Samuel Coavoux et al.

The replication crisis, the failure of scientific claims to be validated by further research, is one of the most pressing issues for empirical research. This is partly an incentive problem: replication is costly and less well rewarded than original research. Large language models (LLMs) have accelerated scientific production by streamlining writing, coding, and reviewing, yet this acceleration risks outpacing verification. To address this, we present an LLM-based system that replicates statistical analyses from social science papers and flags potential problems. Quantitative social science is particularly well-suited to automation because it relies on standard statistical models, shared public datasets, and uniform reporting formats such as regression tables and summary statistics. We present a prototype that iterates LLM-based text interpretation, code generation, execution, and discrepancy analysis, demonstrating its capabilities by reproducing key results from a seminal sociology paper. We also outline application scenarios including pre-submission checks, peer-review support, and meta-scientific audits, positioning AI verification as assistive infrastructure that strengthens research integrity.

, Séverine Carillon, Ibrahima Gueye et al.

Objectives Public awareness and the dissemination of tailored information to lay populations are essential for highly endemic countries like Senegal to achieve hepatitis B elimination targets by 2030. In Senegal, despite its high prevalence, hepatitis B has not received sufficient attention in health communication campaigns compared with other health issues like HIV. We aimed to explore knowledge and perceptions surrounding hepatitis B virus (HBV), as well as the influence of digital media on the information accessed by individuals living with HBV in Senegal.Design We employed a mixed-methods approach combining qualitative semistructured interviews conducted with people living with HBV enrolled in the Senegalese hepatitis B cohort (SEN-B), with a quantitative content analysis of online news coverage focused on HBV within the online media of Senegal.Setting A referral University hospital in Dakar, Senegal.Participants 29 individuals aged >18 years presenting with a positive hepatitis B surface antigen (HBsAg) with a median age of 40 years (IQR 27–54), of whom 51.7% were female.Outcomes and analysis Qualitative interviews were conducted between December 2019 and October 2021, and we employed purposive sampling to select participants enrolled in SEN-B. Thematic analysis facilitated a systematic synthesis of respondents’ narratives. All data analyses were performed using Atlas.ti (V.22). For content analysis of online media news collected from September 2019 to May 2022, a structured data extraction form was developed to collect relevant information from the selected online news articles. Data on readers’ comments spaces were extracted using an inductive approach and were processed using thematic analyses. The quantitative data issued from content analysis were exported to Stata SE V.17.0 (StataCorp) for statistical analysis.Results We observed a generalised lack of knowledge about HBV among participants, some of whom had never heard of the virus prior to their screening. Incomprehension regarding the disease contributed to feelings of fear and anxiety, leading participants to express various concerns about their personal health status, transmission, cure and treatment(s). The presence of rumours surrounding the disease further underscored the limited awareness of HBV revealing the marginal recognition of HBV as a significant societal concern. In many cases, the absence of effective health communication strategies at the national level resulted in individuals turning to traditional and online media for information, which often intensified their fears and concerns about HBV. An analysis of Senegalese media coverage about HBV included 157 articles published between 2009 and 2022. 55.4% (87/157) of these publications appeared in July, coinciding with World Hepatitis Day, while 65.0% (102/157) focused on general HBV epidemiology and activities led by the National Hepatitis Programme. Online media also served as informal spaces where unaccredited actors within the health sector promoted treatments lacking official verification. Additionally, the reactions’ spaces provided a venue for the exchange of information, though without any guarantee of its accuracy.Conclusions Facilitating collaboration and engagement between health communication stakeholders and communities is crucial for effectively disseminating structured information and culturally appropriate messages, ultimately contributing to raising awareness of HBV.

Miguel Ballesteros, Ivan Naumkin, Gabor Toth

The Curie-Weiss model, originally used to study phase transitions in statistical mechanics, has been adapted to model phenomena in social sciences where many agents interact with each other. Reconstructing the probability measure of a Curie-Weiss model via the maximum likelihood method runs into the problem of computing the partition function which scales exponentially with the population. We study the estimation of the coupling parameters of a multi-group Curie-Weiss model using large population asymptotic approximations for the relevant moments of the probability distribution in the case that there are no interactions between groups. As a result, we obtain an estimator which can be calculated at a low and constant computational cost for any size of the population. The estimator is consistent (under the added assumption that the population is large enough), asymptotically normal, and satisfies large deviation principles. The estimator is potentially useful in political science, sociology, automated voting, and in any application where the degree of social cohesion in a population has to be identified. The Curie-Weiss model's coupling parameters provide a natural measure of social cohesion. We discuss the problem of estimating the optimal weights in two-tier voting systems.

Miguel Ballesteros, Ivan Naumkin, Gabor Toth

We study the problem of reconstructing the probability measure of the Curie-Weiss model from a sample of the voting behaviour of a subset of the population. While originally used to study phase transitions in statistical mechanics, the Curie-Weiss or mean-field model has been applied to study phenomena, where many agents interact with each other. It is useful to measure the degree of social cohesion in social groups, which manifests in the way the members of the group influence each others' decisions. In practice, statisticians often only have access to survey data from a representative subset of a population. As such, it is useful to provide methods to estimate social cohesion from such data. The estimators we study have some positive properties, such as consistency, asymptotic normality, and large deviation principles. The main advantages are that they require only a sample of votes belonging to a (possibly very small) subset of the population and have a low computational cost. Due to the wide application of models such as Curie-Weiss, these estimators are potentially useful in disciplines such as political science, sociology, automated voting, and preference aggregation.

Zhun Deng, Thomas P Zollo, Benjamin Eyre et al.

As machine learning models grow increasingly competent, their predictions can supplement scarce or expensive data in various important domains. In support of this paradigm, algorithms have emerged to combine a small amount of high-fidelity observed data with a much larger set of imputed model outputs to estimate some quantity of interest. Yet current hybrid-inference tools target only means or single quantiles, limiting their applicability for many critical domains and use cases. We present QuEst, a principled framework to merge observed and imputed data to deliver point estimates and rigorous confidence intervals for a wide family of quantile-based distributional measures. QuEst covers a range of measures, from tail risk (CVaR) to population segments such as quartiles, that are central to fields such as economics, sociology, education, medicine, and more. We extend QuEst to multidimensional metrics, and introduce an additional optimization technique to further reduce variance in this and other hybrid estimators. We demonstrate the utility of our framework through experiments in economic modeling, opinion polling, and language model auto-evaluation.

Bofu Dong, Pritesh Shah, Sumedh Sonawane et al.

Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware understanding of scientific content. It includes 500 abstracts across five research domains, with manual annotations of event segments, triggers, and fine-grained arguments. We define SciIE as a multi-stage EE pipeline: (1) segmenting abstracts into core scientific activities--Background, Method, Result, and Conclusion; and (2) extracting the corresponding triggers and arguments. Experiments with fine-tuned EE models, large language models (LLMs), and human annotators reveal a performance gap, with current models struggling in domains such as sociology and humanities. SciEvent serves as a challenging benchmark and a step toward generalizable, multi-domain SciIE.

Joao V. Merenda, Gonzalo Travieso, Odemir M. Bruno

Network classification plays a crucial role in the study of complex systems, impacting fields like biology, sociology, and computer science. In this research, we present an innovative benchmark dataset made up of synthetic networks that are categorized into various classes and subclasses. This dataset is specifically crafted to test the effectiveness and resilience of different network classification methods. To put these methods to the test, we also introduce various types and levels of structural noise. We evaluate five feature extraction techniques: traditional structural measures, Life-Like Network Automata (LLNA), Graph2Vec, Deterministic Tourist Walk (DTW), and its improved version, the Deterministic Tourist Walk with Bifurcation (DTWB). Our experimental results reveal that DTWB surpasses the other methods in classifying both classes and subclasses, even when faced with significant noise. LLNA and DTW also perform well, while Graph2Vec lands somewhere in the middle in terms of accuracy. Interestingly, topological measures, despite their simplicity and common usage, consistently show the weakest classification performance. These findings underscore the necessity of robust feature extraction techniques for effective network classification, particularly in noisy conditions.

Hlengiwe P. Maila, Lianne P. Malan, Adrino Mazenda

Background: The public housing delivery practices in South Africa are fragmented, resulting in various outcomes concerning housing delivery. There is a pressing need to overhaul public housing delivery that puts citizens at the core of the delivery process. Aim: The current state-led model of delivering housing is not effective and by design, the model for housing delivery should include the participation of beneficiaries. The aim was to develop a co-production model for housing delivery. Setting: The article focused on the housing sector in South Africa. Methods: A qualitative research approach and grounded theory as research design was used. Instruments were document analysis and semi-structured interviews with participants who are stakeholders in housing co-production. Data collected was analysed through inductive thematic analysis. Results: The results suggested a self-reliant approach to housing delivery, which is demand driven with the state as a facilitator and not the provider of housing. The model for housing should have a component that does not perpetuate a culture of dependency and entitlement but promotes the concept of co-production. Conclusion: The article explored the possibility of introducing a co-production model for housing delivery model in South Africa. It was established that the role of government must shift to that of an enabler and facilitator instead of a provider of housing. Contribution: This proposed model contributes towards the body of knowledge in terms of promoting public service delivery and performance (in this instance in the housing sector) in South Africa as a country situated in Africa.

Kasprzyk Beata

The subject-matter and aim of this paper is to present the extent of poverty and economic and social exclusion in the countries of the European Union (EU-27). The specific aim is the comparative assessment and reduction of these processes between 2011 and 2022.

LOMPO Miyemba

Le Burkina Faso, à l’image d’autres pays de sahel traverse une crise sécuritaire et humanitaire sans précédent provoquée par des attaques de groupes terroristes et autres conflits exposant les femmes et les enfants à des multiples risques et dangers. S’inscrivant dans une démarche mixte, le présent article vise à analyser l’impact de la crise sur le quotidien de la femme déplacée interne. Les résultats de la recherche montrent que les déplacements de population ont renforcé le patriarcat et les violences basées sur le genre. La crise a engendré des stratégies d’adaptation en termes de réorganisation du travail dans les ménages.

Barbara Czarniawska

This article explores Bruno Latour's innovative perspectives on the study of organisations. It goes back to his speech at EGOS in 1993. In it he asked what the sociology of science could contribute to the study of organisation. For Latour, organisation is achieved through interactions that also consider the artefacts that enable interactions to last and to act at a distance. He introduces the idea that organisation is an act of dispatchment: a human or non-human device that follow a script and the connections it establishes. Later, he defended the idea that we can only speak of ‘organisation’ when we have temporarily ceased to organise. Organisations are ‘ghosts’ that appear when the fact of organising, as a mode of existence, disappears. Organisation is inseparable from disorganisation and reorganisation. Organisational scripts circulate between actors who allocate humans and non-humans to accomplish tasks. He suggests adopting an ethnologist's perspective to trace the phenomenon, while ignoring the metalanguage of economists. Organisation begins with the preparation of a script, using the performative powers of fiction, plans and programmes, and continues with the disorganisation and reorganisation associated with their implementation. For Latour, organisations are the effects of the fact of organising, and not the other way round. This highlights the materiality of the arrangements through which scripts are stacked and articulated.

Jacob Hume, Laura Balzano

Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology. In this work, we characterize the celebrated family of spectral methods for static clustering in terms of the low-rank approximation of high-dimensional node embeddings. From this perspective, it becomes natural to view the evolving community detection problem as one of subspace tracking on the Grassmann manifold. While the resulting optimization problem is nonconvex, we adopt a block majorize-minimize Riemannian optimization scheme to learn the Grassmann geodesic which best fits the data. Our framework generalizes any static spectral community detection approach and leads to algorithms achieving favorable performance on synthetic and real temporal networks, including those that are weighted, signed, directed, mixed-membership, multiview, hierarchical, cocommunity-structured, bipartite, or some combination thereof. We demonstrate how to specifically cast a wide variety of methods into our framework, and demonstrate greatly improved dynamic community detection results in all cases.

Yihe Ma

Cooperation is an important research object in economics, sociology, and biology, and the evolution of cooperation in structured populations is a interesting research topic. We mainly focus on the evolution of cooperation with two-order and higher-order game in two-layer network. We introduce a bias coefficient of utility function and study the influence of bias coefficient on the evolution of cooperation in two-layer network. We firstly provide theoretical analysis of fixation probabilities of two-order and higher-order game under weak selection in two-layer network.Secondly,based on the expression of fixation probability, we obtain the critical value of the two different games by comparing the size relationship of fixation probability under weak selection condition and neutral selection condition. Finally, by comparing the relationship between the critical value of single-layer and two-layer network in two-order game and higher-order game, when the nonlinear factor satisfies certain conditions, it is concluded that when the optimal bias coefficient tends towards 0 is met, some two-layer networks promote the evolution of cooperative behavior more than some single-layer networks.

Jinming Li, Gongjun Xu, Ji Zhu

Factor analysis is a widely used statistical tool in many scientific disciplines, such as psychology, economics, and sociology. As observations linked by networks become increasingly common, incorporating network structures into factor analysis remains an open problem. In this paper, we focus on high-dimensional factor analysis involving network-connected observations, and propose a generalized factor model with latent factors that account for both the network structure and the dependence structure among high-dimensional variables. These latent factors can be shared by the high-dimensional variables and the network, or exclusively applied to either of them. We develop a computationally efficient estimation procedure and establish asymptotic inferential theories. Notably, we show that by borrowing information from the network, the proposed estimator of the factor loading matrix achieves optimal asymptotic variance under much milder identifiability constraints than existing literature. Furthermore, we develop a hypothesis testing procedure to tackle the challenge of discerning the shared and individual latent factors' structure. The finite sample performance of the proposed method is demonstrated through simulation studies and a real-world dataset involving a statistician co-authorship network.

I. Reid, M. Weber, H. Gerth et al.

K. Erikson

W. Herberg