How to address Simpson's paradox when aggregating ratios?

Question

I have a table of means and ns from several studies comparing x and y groups. I've also computed the ratio of the means for each row:

# A tibble: 6 × 5
    M_x   n_x   M_y   n_y ratio_xy
  <dbl> <dbl> <dbl> <dbl>    <dbl>
1 11       27  8.11   520     1.36
2  2.61    30  1.2     32     2.17
3  2.48    41  1.29    45     1.92
4  2.9     33  2.41    31     1.20
5  3.96    24  2.48    22     1.60
6  3.4      5  2.78     3     1.22

I'd like to calculate the an "overall" ratio of M_x to M_y across all studies. My first thought was to compute the ratio of the weighted means of x and y:

#> library(dplyr)
#
#> dat %>% 
#+   summarize(
#+     Mw_x = weighted.mean(M_x, n_x),
#+     Mw_y = weighted.mean(M_y, n_y),
#+     ratiow_xy = Mw_x / Mw_y
#+   )
# A tibble: 1 × 3
   Mw_x  Mw_y ratiow_xy
  <dbl> <dbl>     <dbl>
1  4.28  6.82     0.628

Although ratio_xy is > 1 for each individual row, ratiow_xy across all rows is < 1. I assume this is an example of Simpson's paradox, probably driven by the disproportionately large n_y in the first row.

Questions:

1. My intuition is that the rowwise ratios are "right" and the aggregate is "wrong," but my (basic) understanding of Simpson's paradox is it's not so straightforward. How should I interpret these results, or are they interpretable at all?
2. If it's even appropriate, is there another aggregation approach that would capture the direction of ratios in each row?

I've read through some other CV threads on Simpson's paradox (e.g., 1, 2, 3). These have been informative in general, but focus on different kinds of cases, and I'm still unsure how to approach my particular problem.