[johann28]

It's certainly true that definitions are often arbitrary and aren't the "meat of the issue". For example if a field is too obsessed with how it labels things, then it's usually a bad sign.

When in university, some courses would focus very much on definitions and lists of things and what part of the field covers what things, I could tell there was some pretentious bullshit going on. Now mathematics seems like an exception to this, but actually they don't argue about definitions in this sense. If you define your terms slightly differently a mathematician may be annoyed but he will recognize if your overall work is valid.

It's also true on an individual level. I noticed that people who like to argue whether they are programmers or software developers tend to be less concerned about actually getting something done, vs. people who'd say "call me whatever; you can come and watch what I do and decide what you call it".

It comes across as overcompensation for having little to say otherwise. Good scientific papers also don't dwell too much on how to categorize and break up the related fields. But apparently there are people who enjoy defining terms precisely, like whether they do Data Mining, Data Science, Machine Learning or AI or Statistics or Probability. They are all fluid categories and have significant overlaps. There is just no reason to work towards sharp separation.