Tesis Doctorales
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Browsing Tesis Doctorales by Author "Candia Vallejos, Cristian"
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Item Patterns of social interaction in the educational experience analized through a computational social sciences perspective(Universidad del Desarrollo. Facultad de Gobierno, 2024) Oyarzún Wolf, Melanie; Candia Vallejos, Cristian; Rodríguez-Sickert, CarlosThis research explores how social interactions in educational settings influence academic outcomes, cooperation, and group dynamics in primary school classrooms. By applying methods from Computational Social Sciences and Complex Systems, this work integrates three key approaches: network science to measure social structure and student positioning, the collection of behavioral data through field experiments based on game theory applied in the classroom, and econometric analysis to approximate causal effects. The first study examines how reciprocity in social relationships within the classroom enhances academic performance, showing that students who engage in reciprocal interactions achieve better results. The second study investigates how friendship modulates hierarchical relations in public schools, finding that cooperation dynamics among peers are strongly influenced by social status, but friendships can mitigate these hierarchies. The third study focuses on the social interactions of children with Autism Spectrum Disorder (ASD) within classrooms that incorporate both students with and without special educational needs, showing that these students tend to occupy peripheral positions in social networks and engage less frequently in reciprocal relationships. The findings of this project provide a deeper understanding of social relationships in educational contexts, with significant implications for managing classroom dynamics and designing educational policies aimed at improving school coexistence and social inclusion.Item Social Complexity of Performing Arts: Quantifying Gender Inequalities and Career Success in Ballet Through Network Science.(Universidad del Desarrollo. Facultad de Gobierno, 2023) Herrera Guzmán, Yessica; Candia Vallejos, Cristian; Gates, Alexander; Barabási, Albert-LászlóThis thesis explores the application of complex systems research to understand the dynamics of the art world, considering art as a complex system and investigating its various components through data-driven methodologies. By studying art as a complex system, we contribute to a systematic understanding of human development and behavior in creative domains. The social network plays a crucial role in the cultural evolution of art, shaping our cultural identity and collective memory. Analyzing network characteristics provides insights into how individual decisions influence collective dynamics and sustain social phenomena. Previous studies have used network models and data analysis to examine the role of network position and connectedness in artistic collaborations, individual success, and the transmission of artistic knowledge. In this thesis, we focus on ballet as a unique art form with a rich historical and social structure. Ballet provides an opportunity to investigate the role of the social network in shaping collective dynamics in performing arts. We present two research articles that examine gender inequalities and the role of social connections on the career success of ballet dancers. The first article investigates the social network structure of ballet creations and its potential impact on gender disparities in leading positions. The second article explores the influence of social connections and prestige on the career trajectories of ballet dancers, using network analysis and centrality metrics to uncover hierarchical stratification within ballet academies. Our research highlights the significance of social dynamics and network effects in understanding complex social phenomena in the art world. It offers insights into gender inequalities and career success in ballet and demonstrates the value of data-centric methodologies in arts research. By generating a unique dataset and applying interdisciplinary approaches, we contribute to the scientific examination of the arts and enhance our understanding of human creativity and cultural heritage. This thesis contributes to the broader goal of fostering diversity, equity, and inclusion within the arts by shedding light on social structures and suggesting potential avenues for change. Lastly, this work underscores the importance of interdisciplinary research in enriching our understanding of human development and behavior in creative domains.Item The Cost of Being “Late”: Tracing Misalignment and Misdiagnosis Through Relational Architectures(Universidad del Desarrollo. Facultad de Gobierno, 2026) Davyt Colo, Joselina Beatriz; Candia Vallejos, Cristian; Soto-Icaza, PatriciaThe main focus of this thesis is the investigation of the structural origins of temporal disadvantage, manifested as reduced academic persistence and diagnostic delay, across two distinct domains: higher education and clinical diagnosis. Adopting a Computational Social Science framework, we employ network analysis not merely as a tool, but as a theoretical lens to visualize and quantify the relational architectures that generate structural misalignment between individuals and institutions. The first study examines informational inequality in higher education. Using administrative data from 1.6 million applicants in Chile and replicating findings in Portugal, we construct a network of degree preferences to quantify Preference Misalignment—the distance between a student’s true interests and their enrolled program. We demonstrate that this topological distance is a robust predictor of first-year retention: students with misaligned preferences face a significantly higher risk of persistency, a penalty that remains even among high-performing students. The second study addresses recognitional inequality in autism. Analyzing a clinical sample of autistic children without intellectual disability, we integrate bipartite and multilayer networks to decode the mechanisms behind the diagnostic delay in autistic girls (averaging two years later than autistic boys). We identify while structural connectivity (i.e., high betweenness and participation coefficient) accelerates diagnosis in boys, it significantly delays it in girls. Conversely, phenotypic entropy (systemic disorder) facilitates recognition in females. Multilayer analysis further revealed that autistic girls exhibit a differentiated physiological and cognitive architecture, in which high cognitive ability is structurally coupled with increased psychological burden. Together, these findings reveal that being "late" is rarely an individual accident, but a consequence of institutional architectures that render certain profiles illegible. By mapping these invisible structures, this work provides empirical evidence for policy interventions aimed at reducing the structural friction that generates inequality.