A Humane Information Theory

We advance a general theory for mathematically measuring the quality and quantity of information in any textual work meant to be read and processed by humans above all else. This work builds upon a related new contribution to computational poetics-Poetic Measurement Theory (PMT)that was first put forward in the first formal review [6] of a 2012 code poetry anthology featuring 55 poems written using software languages, and which was targeting human readers and not usual machine or program processors. PMT advanced what are technically called intrinsic information measures that do what we can do with Humane Information Theory (HIT), but, unlike HIT, rely especially on parameters derived only from the messages being analyzed. The PMT informational measures PSE and SIM, but also PCE and CIM, operating at symbolic (letter) and conceptual (word) resolution levels respectively, are thus technically classified as "intrinsic". They served well and proved their worth in the comparative analysis of code poetry as demonstrated in that seminal work. However, and as first laid down in the appendix of that review, particularly in HIT, we establish that they would not be very useful in quantifying information quality or quantities for messages written using either entirely different languages or alphabets that share little or no common elements, and thus the need to develop extra analytical mechanics beyond PMT that would allow what are known as "extrinsic" information measures. Moreover, just like with PMT, the measures proposed and developed in HIT make the most sense for human-targeting texts and not just any general or arbitrary messages processable under classical Claude Shannon information theory. Also, HIT manages to advance a measure for information quantity (Humane Information Measure, HIM) that is general enough to apply to both intrinsic and extrinsic measurements, and which, among its special properties, accepts arbitrary entropy measures or is entropy-measure agnostic, so that it can work well with our newly proposed Composition-Sensitive Sequence Entropy (CSSE) that furthers the entropy measure first laid down in the Transformatics 101 thesis of 2025. This work is being published as a standalone paper so that anyone who did not read the code poetry review [6] can readily access the fundamental contributions to information theory and the analysis of humane texts or datasets that were first presented in that book.