THE PROMISE OF SCRIPTURAL REASONING

This Response reintroduces Scriptural Reasoning as portrayed in the essays, showing their various readings and reasonings and how they shed light on SR, while also identifying some lacunae in and between them. The different strengths of these views interact with each other in a highly dynamic and sustaining practice of reading and reasoning Scriptures together, where there is a high degree of mutually responsible hospitality and the possibility of achieving a complex harmony. In conclusion, three tasks for the future of SR are identified: concentrating on the seminal features of the Abrahamic traditions, targeting them and auditing SR performance.     

DOES BELIEF AIM (ONLY) AT THE TRUTH?

It is common to hear talk of the aim of belief and to find philosophers appealing to that aim for numerous explanatory purposes. What belief's aim explains depends, of course, on what that aim is. Many hold that it is somehow related to truth, but there are various ways in which one might specify belief's aim using the notion of truth. In this article, by considering whether they can account for belief's standard of correctness and the epistemic norms governing belief, I argue against certain prominent specifications of belief's aim given in terms of truth, and advance a neglected alternative.     

The Search for Reliable Generalizations About Messages A Comparison of Research Strategies

Two ways of conducting the search for generalizations about messages are considered: Morley's (this volume) proposal that single-message research designs be used, with subsequent meta-analytic summaries, and Jackson and Jacobs's (1983) proposal that multiple-message designs be used, with messages treated as a random factor in the statistical analysis. Jackson and Jacobs's approach is shown to provide a more dependable, efficient, and practical means for gathering the requisite evidence for dependable generalizations. The charge that multiple-message designs suffer from irreparable problems of experimenter bias is refuted. The treatment of messages as a random factor is defended as statistically appropriate and as clearly preferable to the statistical alternatives.