Interrater agreement for reviews for Developmental Review

Reviewer reliability is examined for the first 2 years of operation of Developmental Review. Using the statistic, Finn's r, summary recommendations on publishability are shown to be higher in reliability than previously reported for other journals. Separate rating scales are shown to be less reliable than summary recommendations. Finn's r is compared with the intraclass correlation coefficient and shown to be a better method for examining reviewer agreement across different journals. Potential causes of high reviewer agreement for Developmental Review are discussed, and the importance of the distinction between reliability and validity of reviews is noted.     

Categorizing numeric information for generalization

Learning programs that generalize from real-world examples will have to deal with many different kinds of data. Continuous numeric data can cause problems for algorithms that search for examples with identical property values. These problems can be surmounted by categorizing the numeric data. However, this process has problems of its own. In this paper, we look at the need for categorizing numeric data and several methods for doing so. We concentrate on the use of generalization-based memory, a memory organization where actual examples are stored along with generalizations, which leads to a generalization-based categorization algorithm. We also consider how to use a number heuristic, looking for gaps. These methods have been implemented in the UNIMEM computer system. Examples are presented of these algorithms categorizing data about the states of the United States.