def METHOD_NAME(user_ranking: npt.ArrayLike, consensus_ranking: npt.ArrayLike, voter_data: Voter) -> Voter:
    """
    This function returns the gain of points for a given ranking's votes
    This function is only to be run when archiving a question
    i.e. the question has had sufficiently many votes, or we can't get more than "K" bits of information
    we use the bubble-sort distance (or "kendall-tau" distance) to compare the two rankings
    we use this over spearman correlation since:
        "[Kendall's τ] approaches a normal distribution more rapidly than ρ, as N, the sample size, increases;
            and τ is also more tractable mathematically, particularly when ties are present"
    Gilpin, A. R. (1993). Table for conversion of Kendall's Tau to Spearman's
     Rho within the context measures of magnitude of effect for meta-analysis
    Further in
        "research design and statistical analyses, second edition, 2003"
    the authors note that at least from an significance test POV they will yield the same p-values
        Parameters:
            user_ranking (ArrayLike): ranking produced by the user
            consensus (ArrayLike): ranking produced after running the voting algorithm to merge into the consensus ranking
            voter_data (Voter): a "Voter" object that represents the person that wrote the prompt
        Returns:
            updated_voter (Voter): the new "quality score" and points for the voter
    """
    bubble_sort_distance, p_value = kendalltau(user_ranking, consensus_ranking)
    # normalize kendall-tau from [-1,1] into [0,1] range
    bubble_sort_distance = (1 + bubble_sort_distance) / 2
    new_points = bubble_sort_distance + voter_data.ranking_points
    new_good_rankings = int(bubble_sort_distance > 0.5) + voter_data.num_good_rankings
    new_num_rankings = voter_data.num_rankings + 1
    return replace(
        voter_data,
        num_rankings=new_num_rankings,
        num_good_rankings=new_good_rankings,
        ranking_points=new_points,
    )