Computer-Mediated Communication and Ecclesiological Challenges To and From the Reformed Tradition

Communities of faith have appeared online since the inception of computer -­ mediated communication (CMC)and are now ubiquitous. Yet the character and legitimacy of Internet communities as ecclesial bodies is often disputed by traditional churches; and the Internet's ability to host the church as church for online Christians remains a question. This dissertation carries out a practical theological conversation between three main sources: the phenomenon of the church online; ecclesiology (especially that characteristic of Reformed communities); and communication theory. After establishing the need for this study in Chapter 1, Chapter 2 investigates the online presence of Christians and trends in their Internet use, including its history and current expressions. Chapter 3 sets out an historical overview of the Reformed Tradition, focusing on the work of John Calvin and Karl Barth, as well as more contemporary theologians. With a theological context in which to consider online churches in place, Chapter 4 introduces four theological themes prominent in both ecclesiology and CMC studies: authority; community; mediation; and embodiment. These themes constitute the primary lens through which the dissertation conducts a critical-­confessional interface between communication theory and ecclesiology in the examination of CMC. Chapter 5 continues the contextualization of online churches with consideration of communication theories that impact CMC, focusing on three major communication theories: Narrative Theory; Interpretive Theory; and Speech Act Theory. Chapter 6 contains the critical conversation between ecclesiology and communication theory by correlating the aforementioned communication theories with Narrative Theology, Communities of Practice, and Theo-­Drama, and applying these to the four theological themes noted above. In addition, new or anticipated developments in CMC investigated in relationship to traditional ecclesiologies and the prospect of cyber-­ecclesiology. Chapter 7 offers an evaluative tool consisting of a three-­step hermeneutical process that examines: 1) the history, tradition, and ecclesiology of the particular community being evaluated; 2) communication theories and the process of religious-­social shaping of technology; and 3) CMC criteria for establishing the presence of a stable, interactive, and relational community. As this hermeneutical process unfolds, it holds the church at the center of the process, seeking a contextual yet faithful understanding of the church.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.