Diversity statement texts - create template to guide analysis

[LukasWallrich]

We need to agree on the specific template text we want to populate so that we can make sure to get the right data out of the analysis.

For now, here the most common statement as per Zurn et al. (2020):

Recent work in several fields of science has identified a bias in citation practices such that papers from women and other minorities are under-cited relative to the number of such papers in the field [2., 3., 4., 5., 6.]. Here we sought to proactively consider choosing references that reflect the diversity of the field in thought, form of contribution, gender, and other factors. We obtained predicted gender of the first and last author of each reference by using databases that store the probability of a name being carried by a man or a woman [4]i. By this measure (and excluding self-citations to the first and last authors of our current paper), our references contain 42.9% woman(first)/woman(last), 28.6% man/woman, 7.1% woman/man, and 21.4% man/man. This method is limited in that: (i) names, pronouns, and social media profiles used to construct the databases may not, in every case, be indicative of gender identity, and (ii) it cannot account for intersex, non-binary, or transgender people. We look forward to future work that could help us to better understand how to support equitable practices in science.

This only considers first and last authors and requires knowing the gender of these. In addition, at least, I would be keen to report the gender of all, especially because the last author only matters in some scientific fields.

[LukasWallrich]

An interesting addition from Rust & Mehrpour (2020) is a benchmark sentence (we can't create that for all fields, but might want to document the suggestion):

Expected proportions estimated from 5 top neuroscience journals, as reported in [70], are 58.4% man/man, 9.4% man/woman, 25.5% woman/man, and 6.7% woman/woman.

[LukasWallrich]

Also, single-authored papers might need to be split from the MM and WW combinations:

By this measure, our references are written by woman (first author)/woman (last author)— 25% (including 14% solo female author), 14% by men (first)/ woman (last), 10% by woman (first)/ man (last), and 50% by man (first)/man (last) (including 30% solo male author).