If given a dataset (e.g. InsuranceQA) with a list of questions and a list of passages of candidate answers (each question can be matched with multiple candidate answers) and the task is to rank the top 10 (the ordering is not labeled) most relevant answers (passages) given a query.

I have been reading about possible ways to solve this problem and came across both Answer Selection and Answer Re-ranking techniques.

I would like to know in terms of a NLP tasks, what is the difference between Answer Selection and Answer Re-Ranking?

I am thinking that Answer Re-ranking is where you first use something like BM25 to retrieve a list of say 1000 candidate answers then ranking the relevant answers whereas Answer Selection can use re-ranking to achieve this or not.