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Source: https://writers.stackexchange.com/a/37931
License name: CC BY-SA 3.0
License URL: https://creativecommons.org/licenses/by-sa/3.0/

Source: https://writers.stackexchange.com/a/37931
License name: CC BY-SA 3.0
License URL: https://creativecommons.org/licenses/by-sa/3.0/

Yes, these sorts of comparisons between systems are very difficult, essentially because there is no independent definition of terms like topic and module outside of the particular systems that use them. In other words, both systems, as well as several other similar systems, use similar terms to describe their models and functions quite independent of each other. It is not like comparing two minivans from different manufacturers. There is no agreed definition of horsepower or steering wheel or door that you can use to compare them with. You basically have to get down to the nitty gritty implementation details and see how they differ.

In my forthcoming book, _Structured Writing: Rhetoric and Process_, I try to provide some tool-neutral terminology for talking about structured writing methods, largely by avoiding these confusing terms altogether. When it comes to terms like module and topic, I suggest four types of information block: the semantic block (objects that writers would recognize independent of a structured writing system, like lists and tables), the structural block (the things semantic blocks are made from), the information typing block (the kind of blocks found in information typing theory, such as Information Mapping), and the rhetorical block (the thing that is actually meant to be read).

But even with those terms, I could not tell you which of these things a DITA topic is, because the term is just not well defined. Depending on how you use it, it could be any one of my four types. I don't know S1000D well enough to say how their idea of a module would fit, but I suspect it would be similarly vague.

Content, fundamentally, has fuzzy boundaries. It is all the stuff that does not fit neatly into data structures. Structured writing tries to make content look enough like a data structure to be processable by algorithms, but if you try to do that in a general way, you end up making your containers pretty fuzzy in order to fit around all the varied things they have to contain. And thus those containers tend to defy both characterization and comparison.

If you actually want containers that can be defined and compared with any degree of strictness, you have to be much more specific to the subject matter, audience, and type of document you are dealing with.

Original score: 4