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Socio-Technical Exchange with Machines: Worker Experiences with Complex Work Technologies
No full textMachines are now commonplace work partners and Human-Machine Communication (HMC) is part of modern work. Guided by the machine heuristic and Social Exchange Theory (SET) this paper uses interviews with 22 workers to explore the socio-technical relationships between humans and machines at work. Participants worked with complex machines across a wide variety of industries and roles and findings reveal participants see machines as part of the work ecosystem, develop everyday machine heuristics to guide repeated interactions with machines, and that elements of human uniqueness persist in evaluations of humans and machines. Discussion points emphasize situated and developing human-machine interaction scripts, the value and limits of social theory to HMC interaction, and illuminate socio-technical exchange as an emergent theory
Transformations and Revelations: The Communicative Constitution of Trustworthiness and Trust Through AI Development Practices
No full textThis study explores AI development practices to understand how trustworthiness is built into AI systems, and how this generates trust in AI. Through a multi-sited ethnography based methodology, we analyze observations, interviews, and documentation from AI developers working on trustworthy AI. Our analysis shows two key practices: transformation and revelation. Through transformational AI development practices trustworthiness is (re)constituted, though more or lesser degrees. Through revelation practices, AI developers communicatively engage with others to generate trust. This focus on developers adds to the user-centric perspective and shows the role nontechnical development practices have in shaping trust and trustworthy AI before it is implemented. Policy guidelines lack clarity on nontechnical aspects, so we argue that further attention on communication can benefit AI practice and policy
Development and Validation of the Attitudes Toward Algorithms Scale: A Universal Scale to Measure Individuals Attitudes Toward Algorithmic Decision-Making
No full textAlgorithmic decision-making (ADM) has become increasingly important across various fields, ranging from personal entertainment to criminal justice. While existing research has predominantly explored evaluative outcomes like individuals’ perceptions and interactions with these systems, there remains a significant gap in understanding the underlying mechanisms that lead to these outcomes. Building on traditional behavioral intention theories, we identified attitudes toward algorithms and ADM as crucial determinants. In response to the lack of an appropriate measure for this construct, we developed and validated the Attitudes Toward Algorithmic Decision-Making Scale (ATAS), a novel instrument designed to assess core beliefs that drive attitude formation toward ADM across three studies. Using this scale, we show that laypeople’s attitudes toward ADM are driven by beliefs about the objectivity, ethicality, and performance of such systems and are characterized by ambivalence. We further discuss potential applications of the scale for future research and how well-established technology acceptance frameworks can benefit from it
Being and Becoming in Human-Machine Communication: Core Commitments and Conceptual Foundations of a Trans-Ontological Field
No full textThis introduction traces the emergence of Human-Machine Communication (HMC) as a distinct field centered on communication across ontological boundaries. Arguing that HMC is not merely about interacting with machines but about how communicative presence and legitimacy are constituted, the authors introduce the Act–Mean–Relate (AMR) paradigm to conceptualize how machines’ capacity for action, signification, and relation enable communication. HMC challenges human exceptionalism by shifting focus from internal states and categorical differences to symbolic and relational dynamics. It addresses the interpretive labor involved in making machine others intelligible and emphasizes communication as the co-construction of social reality. The article highlights tensions between computational and relational logics and treats design as a third party in communicative triads. In doing so, it frames HMC as a pluralistic, trans-ontological field that advances communication theory while attending to the ethical, epistemic, and material implications of human-machine communication
Analysis of Numerical Methods for SPDEs, and Applications of Free Boundary Problems in Financial Mathematics
No full textThis dissertation studies the key properties of a fully discrete finite element method for a class of stochastic moving boundary problems. Then, free boundary problems and their applications in finance are explored.
The first part of this thesis studies the properties of a proposed fully discrete finite element method scheme with an interpolation operator for stochastic Cahn-Hilliard equations with functional-type noise. These solutions cannot be differentiated in time, so we provide Holder continuity results to aid in the scheme’s error analysis. We further derive the uniform boundedness of higher-order L2-norm moment for use in the analysis. Then nearly optimal convergent rates proven based on a set of probability 1, which could be dropped in future work. Stability and error analysis results are demonstrated through four numerical tests using FEniCS, to show consistency between these theoretical results and computation.
Next, free boundary problems are required when the domain of a moving interface problem changes and needs to be solved as part of the solution. The prototypical example is the Stefan problem, which has its origin in the interesting behaviors occurring in the melting and formation of flat ice. We will sketch a proof of an important regularity result in the theory of free boundary problems, particularly the Obstacle problem. These problems are naturally optimal stopping problems and gained more and more attention in recent years in financial mathematics, which we then discuss
Instructional and Motivational Factors Predicting Deep Learning Approaches in Pre-Clinical Medical Students
No full textPre-clinical medical students predominantly employ surface learning approaches, focusing on memorization and exam preparation rather than developing a deep understanding of medical knowledge. This study employed a correlational design comparing two courses with different instructional approaches using expectancy-value theory to examine how instructional methods and motivational factors predict deep learning approaches among 148 pre-clinical medical students at a College of Medicine in the Southeastern United States. Initial results showed that the three separate constructs in task motivation (expectancy beliefs, values, and cost) did not mediate the relationship between active instructional methods and deep learning strategies. Instead, active learning was a statistically significant predictor of deeper learning strategies (β = 0.25, p = .002). While active learning significantly impacted students\u27 perceptions of cost (β = -0.46, p \u3c .001) and value (β = 0.19, p = .02), the paths from cost and value to deep learning were not significant (cost: β = -0.03, p = .79; value: β = 0.12, p = .16). Thus, while active learning predicted lower perceived cost and increased value, these changes did not predict greater use of deep learning strategies. However, post-hoc analysis revealed that a linear combination of expectancy, value, and cost fully mediated the relationship between active instruction and learning approaches (β = .15, z(143) = 3.48, p = \u3c .001). In this model, the direct effect of active instruction on learning strategies was statistically non-significant (β = .06, z(143) = .83, p = .41). These findings suggest that active instructional methods enhance pre-clinical medical students\u27 task motivation, which in turn increases students\u27 adoption of deep learning approaches. Medical educators can use these findings to design and implement instructional activities and materials that promote student task motivation and a deeper understanding of medical knowledge
Florida Frontiers Radio Program #602
No full textSEGMENTS | The Influenza Pandemic of 1918-19 | Philanthropist and Humanitarian Eartha Whit
March is full of Madness
No full textToday is the first day of the Men’s Sweet Sixteen in the Moveable Feast called March Madness. It comes in an extended weekend of what might be the maddest March in history. No, I am not talking politics. I am talking about sports in America and a television schedule that could burn out the circuitry on TV sets across the country
Urban Isolation in the Digital Age- Examining the Sociological Impact of the Digital Divide on Civic Life in U.S. Cities
No full textThe digital divide remains a persistent barrier to civic participation and social inclusion in urban America, particularly in historically underserved communities. This mixed-methods study investigates how digital lack of access affects civic engagement, social capital, and community cohesion in U.S. cities, with a focus on Chicago’s South Side, Harlem New York, St. Louis, and Orlando. Drawing on quantitative data from recent surveys and qualitative insights from peer-reviewed literature, the research applies four sociological frameworks, Urban Sociology, Social Capital Theory, Critical Race Theory, and Symbolic Interactionism. These were used to examine the structural and interpretive aspects of digital isolation. Findings reveal that limited access to broadband and digital literacy disproportionately affects low-income and racialized populations, reducing their participation in voting, advocacy, and public discourse. The study also highlights community-based strategies for maintaining civic ties in digitally disconnected neighborhoods. Policy recommendations emphasize the need for equitable broadband infrastructure, digital literacy programs, and inclusive smart city planning. This research contributes to a deeper understanding of how digital inequality reinforces urban marginalization and offers actionable pathways toward digital justice
Living Shoreline Prioritization and Hydrodynamic Habitat Suitability Models for Restoration in the Indian River Lagoon and Lake Worth Lagoon, Florida
No full textThe spatial dataset: UCF_shorelinemodel_IRL_LWL.shp, was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon and Lake Worth Lagoon.
The dataset presented herein includes: shoreline data from \u3e15,000 transects collected in the field, shoreline wave climate data created through hydrodynamic modeling and frequency analysis of historic data, categorization of potential risk for shoreline boat wake impact, persistence of seagrass within 210 m of the shoreline, model outputs of shorelines prioritized for stabilization/restoration based on field data, and model outputs of shoreline hydrodynamic habitat suitability for mangroves and seagrass, created by combining hydrodynamic modelling outputs and field survey data