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Compared to an earlier version of this paper that I reviewed, the author has made major improvements. The paper is much more focused now and much easier to read.

Below I present my comments on the paper. I don’t have much expertise on the theoretical aspects of the paper. My comments focus on the methodological aspects.

RESP values calculated using the formula presented in Section 3 are bounded between -1 and +1, not between -100 and +100. To obtain values bounded between -100 and + 100, RESP values need to be multiplied by 100. It seems this multiplication incorrectly has not been included in the formula presented in Section 3.

The ideal-type density functions presented in Figure 1 need a stronger rationale. The density function for strong heteromorphism seems incorrect to me. In a given discipline, the strongest heteromorphism is obtained when one university fully dominates the others. This means that v = 0 for all universities except for one. The latter university is the only university that has activity in the discipline. In this situation, RESP = -100 for all universities except for one. The latter university has a RESP value that is close to +100. Hence, the strongest form of heteromorphism does not result in the density function presented in the rightmost plot in Figure 1. Instead, it results in a highly skewed density function. Most of the density is concentrated on -100 while the density for positive RESP values is very low (somewhat similar to the results for grants and technical universities reported in Figure 2). This argument shows that the density function presented in Figure 1 for strong heteromorphism is not correct. I also doubt the correctness of the density function for maximum entropy. I would like to see a detailed argument that explains how maximum entropy results in this density function.

The sigma rule introduced in Section 3 seems problematic to me. Consider a discipline in which some universities have a positive RESP value at time t_1 while others have a negative RESP value. The average RESP value equals 0. Now suppose that for each university in this discipline the RESP value at time t_2 is twice as large as the RESP value at time t_1, that is, for each university RESP(t_2) = 2 * RESP(t_1). In this situation, the standard deviation of the RESP values is also two times larger at time t_2 than at time t_1. This then means that the number of universities that have a distance from the mean that is smaller than the standard deviation is the same at times t_1 and t_2. Hence, according to the sigma rule, the conclusion would be that the degree of isomorphism is the same at times t_1 and t_2. This conclusion is incorrect. Obviously, the degree of isomorphism is higher at time t_1 than at time t_2, since the RESP values are closer to 0 at time t_1 than at time t_2.

Finally, I was unable to find the data set used by the author. To make sure the research can be reproduced, I would like to ask the author to publish the data set and to include a data availability statement and a reference to the data set in the paper.