This paper presents an operative measure of reinforcement for constructive learning Ε½ . methods, i.e., eager learning methods using highly expressible or universal representation languages. These evaluation tools allow a further insight in the study of the growth of knowledge, theory revision, and abduction. The final approach is based on an apportionment of credit wrt the ''course'' that the evidence makes through the learned theory. Our measure of reinforcement is shown to be justified by cross-validation and by the connection with other successful evaluation criteria, like the minimum description length principle. Finally, the relation with the classical view of reinforcement is studied, where the actions of an intelligent system can be rewarded or penalized, and we discuss whether this should affect our distribution of reinforcement. The most important result of this paper is that the way we distribute reinforcement into knowledge results in a rated ontology, instead of a single prior distribution. Therefore, this detailed information can be exploited for guiding the space search of inductive learning algorithms. Likewise, knowledge revision may be done to the part of the theory which is not justified by the evidence.