Not available / text

This investigation of the 2014 midterm election brings together research in communication, journalism, political science, and sociology. Incorporating concepts of interdependence through the application of social network analysis, I analyzed how journalists construct and utilize networks of news sources in election coverage. Survey results indicate that journalists use sources in a complementary fashion in order to fulfill their resource needs. Such perceptions are also dependent on aspects of the journalist, news norms, organizational pressures, and extra-media influences. Overall, source networks are strategically used to fulfill traditional journalistic norms of objectivity and credibility as well as economic and entertainment needs. Content analysis results suggest that, along with more traditional variables such as ownership and source type, networks characteristics also influence new outcomes, with higher network density resulting in increases in issue coverage. In other words, greater connections among sources may result in more substantive information being passed on to voters. For the field of communication, this study enhances our understanding of agenda building and framing by revealing (1) how journalists perceive their networks (networks to journalists), (2) how they translate real life networks into symbolic networks within news coverage (networks from journalists), (3) how source networks vary across time and election contexts (networks across time), and (4) how such networks influence coverage outcomes. It furthers our understanding of the role interdependence plays in news construction and suggests the incorporation of network concepts and measures will increase our understanding of the news production process.

No description available.

Technologically mediated listening has changed the way in which music is heard as well as the way in which musical communities are constructed. Communities are no longer necessarily tied to place, and in the case of virtual communities, musicians can create a sense of community and a sense of place through their interactions. Some virtual communities of musicians - specifically those that specialize in electronic music - are ideally situated in cyberspace; what a producer of electronic music hears in his or her headphones when composing music is exactly what the audience hears after downloading or streaming it. The music remains in a digital format from its conception to its reception.In a Brazilian virtual community of electronic musicians called EnergyBR.net, fans, DJs, and producers exchange ideas about music, creating a feedback loop. In EnergyBR.net, this cyber-feedback loop shapes musical creation as well as a sense of place and community.

Little is known about the communication principles necessary for the design and implementation of health information technology (HIT) that supports the needs of healthcare providers from multiple professions. The purpose of this descriptive, exploratory research was to examine the patterns and methods of communication used by nurses, physicians, social workers, respiratory therapists, and pharmacists to share patient information during a patient handoff between units. The principles of complexity science were used as a theoretical framework and an original model of the healthcare organization consisting of embedded complex adaptive systems is presented.Five patient handoffs from the emergency department to participating inpatient units were included in the study. Providers responsible for the care of patients during the designated handoffs were identified through observation and snowball sampling and asked to complete a survey asking whom they communicated with and how. Social Network Analysis was used to map, analyze, and compare the communication patterns used by healthcare providers. Inferential statistics and thematic content analysis were used to examine provider characteristics and satisfaction with the quality of information available.The multi-professional collaborative patterns that emerged revealed the simultaneous use of both synchronous and asynchronous communication methods. HIT was shown to play a major role in the coordination process. Centrality and centralization measures identified that there is no one particular professional group dominating communication and hierarchy metrics indicate a unidirectional communication flow with tiers of dominant providers filtering information to providers on the lower tiers. These patterns suggest that the coordination of patient care during a handoff is a complex process that is the domain of more than one professional group.Satisfaction with the quality of available information was higher for providers working in the ED compared to the admitting units. Verbal communication was preferred by most participants despite difficulties identifying or contacting providers in other units. This study provides a foundation for future research that examines how communication principles that reflect the needs of multiple providers can be incorporated into healthcare provider workflow and HIT design.

This dissertation is an empirical study of the actors and organizations working in criminal and drug courts. Specifically, the dissertation examines the structure (as defined by the interactions and relationships of players) of a criminal court and a drug court operating under a state trial court system in the United States. Recent reforms to trial courts indicate that the organizational structure of a typical trial court has changed in many states. Separately, specialty courts which help coordinate treatment for offenders like drug users and mental patients in many jurisdictions have changed the structure, process, and the nature of trial courts.The study is an inductive study using a case method research strategy to build new theory from past findings of organizational studies of criminal courts and from the little we know about drug courts as organizations. The method of inquiry in the study is a triangular research strategy that incorporates both qualitative and quantitative data collection and analysis. The qualitative data collection methods include primarily participant observations of drug team meetings and court proceedings, and semi-structured interviews with actors representing organizations participating in both criminal and drug courtrooms. The study uses network analysis as the primary method for analyzing quantitative data. The research site is the Arizona Superior Court in Pima County, located in Tucson, Arizona.I found that the most important central actors across all phases of the criminal court case disposition process are judges, prosecutors, and defense attorneys, and that measuring core workgroup actors across all phases give a more accurate picture of the criminal court case disposition process. I also found that defense attorneys may be less familiar with other court actors than prosecutors because they may enter the criminal justice system from many different sponsoring organizations. As for the drug court case disposition process, the study shows that the most central player is not always the judge. In addition, the study reveals that drug courts, as court reforms, have little overall connection to overall criminal court organization. Important policy implications and theory inferences, as well as recommendations for future court studies, are discussed.

This dissertation focuses on the performance analysis of multiclass open queueing networks using semi-martingale reflecting Brownian motion (SRBM) approximation. It consists of four parts. In the first part, we derive a strong approximation for a multiclass feedforward queueing network, where jobs after service completion can only move to a downstream service station. Job classes are partitioned into groups. Within a group, jobs are served in the order of arrival; that is, a first-in-first-out (FIFO) discipline is in force, and among groups, jobs are served under a pre-assigned preemptive priority discipline. We obtain an SRBM as the result of strong approximation for the network, through an inductive approach. Based on the strong approximation, some procedures are proposed to approximate the stationary distribution of various performance measures of the queueing network. Our work extends and complements the previous work done on the feedforward queueing network. The numeric examples show that the strong approximation provides a better approximation than that suggested by a straightforward interpretation of the heavy traffic limit theorem. In the second part, we develop a Brownian approximation for a general multiclass queueing network with a set of single-server stations that operate under a combination of FIFO (first-in-first-out) and priority service disciplines and are subject to random breakdowns. Our intention here is to illustrate how to approximate a queueing network by an SRBM, not to justify such approximation. We illustrate through numerical examples in comparison against simulation that the SRBM model, while not always supported by a heavy traffic limit theorem, possesses good accuracy in most cases, even when the systems are moderately loaded. Through analyzing special networks, we also discuss the existence of the SRBM approximation in relation to the stability and the heavy traffic limits of the networks. In most queueing network applications, the stationary distributions of queueing networks are of great interest. It becomes natural to approximate these stationary distributions by the stationary distributions of the approximating SRBMs. Although we are able to characterize the stationary distribution of an SRBM, except in few limited cases, it is extremely difficult to obtain the stationary distribution analytically. In the third part of the dissertation, we propose a numerical algorithm, referred to as BNA/FM (Brownian network analyzer with finite element method), for computing the stationary distribution of an SRBM in a hypercube. SRBM in a hypercube serves as an approximate model of queueing networks with finite buffers. Our BNA/FM algorithm is based on finite element method and an extension of a generic algorithm developed in the previous work. It uses piecewise polynomials to form an approximate subspace of an infinite dimensional functional space. The BNA/FM algorithm is shown to produce good estimates for stationary probabilities, in addition to stationary moments. This is in contrast to the BNA/SM (Brownian network analyzer with spectral method) developed in the previous work, where global polynomials are used to form the approximate subspace and they sometime fail to produce meaningful estimates of these stationary probabilities. We also report extensive computational experiences from our implementation that will be useful for future numerical research on SRBMs. A three-station tandem network with finite buffers is presented to illustrate the effectiveness of the Brownian approximation model and our BNA/FM algorithm. In the last part of the dissertation, we extend the BNA/FM algorithm to calculate the stationary distribution of an SRBM in an orthant. This type of SRBM arises as a Brownian approximation model for queueing networks with infinite buffers. We prove the convergence theorems which justify the extension. A three-machine job shop example is presented to illustrate the accuracy of our extended BNA/FM algorithm. In fact, this extended algorithm is also used in the first two parts of this dissertation to analyze the performance of several queueing network examples and it gives fairly good performance estimates in most cases.

No description available.

No description available.

No description available.