Making Sadza with Deaf Zimbabwean Women: A Missiological Reorientation of Practical Theological Method toward Self-Theologizing Agency among Subaltern Communities

Missiological calls for self-theologizing among faith communities present the field of practical theology with a challenge to develop methodological approaches that address the complexities of cross-cultural, practical theological research. Although a variety of approaches can be considered critical correlative practical theology, existing methods are often built on assumptions that limit their use in subaltern contexts. This study seeks to address these concerns by analyzing existing theological methodologies with sustained attention to a community of Deaf Zimbabwean women struggling to develop their own agency in relation to child rearing practices. This dilemma serves as an entry point to an examination of the limitations of existing methodologies and a constructive, interdisciplinary theological exploration. The use of theological modeling methodology employs my experience of learning to cook sadza, a staple dish of Zimbabwe, as a guide for analyzing and reorienting practical theological methodology. The study explores a variety of theological approaches from practical theology, mission oriented theologians, theology among Deaf communities, and African women’s theology in relationship to the challenges presented by subaltern communities such as Deaf Zimbabwean women. Analysis reveals that although there is much to commend in these existing methodologies, questions about who does the critical correlation, whose interests are guiding the study, and consideration for the cross-cultural and power dynamics between researchers and faith communities remain problematic for developing self-theologizing agency. Rather than frame a comprehensive methodology, this study proposes three attitudes and guideposts to reorient practical theological researchers who wish to engender self-theologizing agency in subaltern communities. The creativity of enacted theology, the humility of using checks and balances in research methods, and the grace of finding strategies to build bridges of commonality and community offer ways to reorient practical theological methodologies toward the development of self-theologizing agency among subaltern people. This study concludes with discussion of how these guideposts can not only benefit particular work with a community of Deaf Zimbabwean women, but also provide research and theological reflection in other subaltern contexts.

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.