You’re making assumptions about how they work based on your intuition - luckily we don’t need to do much guesswork about how the sorts are actually implemented because we can just look at the code to check:

CREATE FUNCTION r.scaled_rank (score numeric, published timestamp with time zone, interactions_month numeric)
    RETURNS double precision
    LANGUAGE sql
    IMMUTABLE PARALLEL SAFE
    -- Add 2 to avoid divide by zero errors
    -- Default for score = 1, active users = 1, and now, is (0.1728 / log(2 + 1)) = 0.3621
    -- There may need to be a scale factor multiplied to interactions_month, to make
    -- the log curve less pronounced. This can be tuned in the future.
    RETURN (
        r.hot_rank (score, published) / log(2 + interactions_month)
);

And since it relies on the hot_rank function:

CREATE FUNCTION r.hot_rank (score numeric, published timestamp with time zone)
    RETURNS double precision
    LANGUAGE sql
    IMMUTABLE PARALLEL SAFE RETURN
    -- after a week, it will default to 0.
    CASE WHEN (
now() - published) > '0 days'
        AND (
now() - published) < '7 days' THEN
        -- Use greatest(2,score), so that the hot_rank will be positive and not ignored.
        log (
            greatest (2, score + 2)) / power (((EXTRACT(EPOCH FROM (now() - published)) / 3600) + 2), 1.8)
    ELSE
        -- if the post is from the future, set hot score to 0. otherwise you can game the post to
        -- always be on top even with only 1 vote by setting it to the future
        0.0
    END;

So if there’s no further changes made elsewhere in the code (which may not be true!), it appears that hot has no negative weighting for votes <2 because it uses the max value out of 2 and score + 2 in its calculation. If correct, those posts you’re pointing out are essentially being ranked as if their voting score was 2, which I hope helps to explain things.

It’s a simplified version they had explained somewhere in the documentation. Details like that may be left out.

If you want a bit more detail, look at my edit. The functions to calculate the hot and scaled for content is now there.