Using uncommon abbreviations
Something which I see all the time in (popular) science writing is the use of abbreviations to indicate concepts. For (a made up) example:
So when we're dealing with Anachronistic Meta Mechanics (AMM) we have to take a wholly different approach than before in the case of Amniotic Uber Psychotics (AUP). Those in favour of AMM in fact frequently disagree with the conclusions reached by applying AUP mechanisms to the same data set...
Since I'm not familiar with these abbreviations from prior experience I always end up looking back to where the abbreviations were first introduced. This severely impedes the progress I can make across a text like this. However, since it is an extremely common practice I figured I'd ask if this is a problem more people have and whether there are good alternatives.
Stanislaw Lem had a very nice method for that. Read his "Observation on the Spot" for it, although I'm not sure if trans …
12y ago
If they are abbreviations which are extremely common to the field, once per work is often enough to define them. If they …
13y ago
This post was sourced from https://writers.stackexchange.com/q/1693. It is licensed under CC BY-SA 3.0.
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If they are abbreviations which are extremely common to the field, once per work is often enough to define them. If they are rare, invented for the piece, or really jargon, I would say once per section (once per chapter, once per web page).
Alternatively, a list of acronyms at the beginning or end of a piece might also be helpful.
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Stanislaw Lem had a very nice method for that. Read his "Observation on the Spot" for it, although I'm not sure if translation captures the spirit.
In essence, the acronyms compose into meaningful, half-meaningful, humorous, horribly misspelled, rudely suggestive and otherwise very memorable words.
So when we're dealing with SuperChronistic Mechanics (SuC-Me) we have to take a wholly different approach than before in the case of Temporally InterTransmissive Area Oligarchs (TIT/AreOli). Those in favour of SuC-Me in fact frequently disagree with the conclusions reached by applying TIT mechanisms to the same data set...
This post was sourced from https://writers.stackexchange.com/a/6841. It is licensed under CC BY-SA 3.0.
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