The problem is none of this matters since we don't know anything about the people rating the photos. Not their sex, not their age, not their location nothing.
To wit:
"and nothing about users who judge the photos."
Which is especially problematic since user generated ratings are ordinal, not interval data. Since the idea of an interval between points in ordinal data is essentially meaningless the summary statistics you mentioned are not meaningful either.
It's one thing for Amazon to come up with a mean user rating to give you a sense of how people like something, but it's not a valid method of comparing the data we have here, especially when the differences are so small
E.g. in a model that predicts students' GPA, you could divide your data into a hierarchy consisting of, at the highest level, geographic area, followed by high school, maybe followed by teacher. In that model, the correlation between students who are in the same state, the same school or in the same classroom would be accounted for. You could even go as deep as at an individual level if you have >1 observation per student.
In addition to regular predictive variables, judg.me could probably use their weblogs to group people's judgement scores by country of origin and by individuals, among other possibilities.
You should also check intra-coder reliability, i.e. give the same person the same set of photos with two weeks or so between. You can then again calculate the correlation. This tells you wether your categories are too fuzzy (e.g. what exactly is medium length hair?).
All in all this has serious methodical flaws, from a social science perspective it’s not salvageable, and I haven’t even talked about the complete lack of statistical tests (which, to be honest, would just be like polishing a turd).
Second, there is a huge problem with causality here. So for instance, the author writes: "Be Asian if you want to appear smart; Latino if you want to appear extroverted." The problem is that there is a methodological flaw. On the first photo I saw on judge.me, I was presented with this image: http://images.judg.me/82e7fcbd988dbdcac0d00bd53fb93e96.jpg This would appear to me to be a latino or hispanic male at a party. I'm highly inclined to rate them highly on the extrovert scale: they're at a party. But that doesn't indicate stereotypically latino or hispanic features indicate extroversion. It could be that people with stereotypically latino or hispanic features were more likely to upload photos in which the image portrayed a more stereotypically extroverted activity.
Third, it appears that users can upload a photo to the site and see their feedback from votes. It seems highly possible that users self-select a photo that will best affirm the image of themselves they wish to cultivate. In that respect, there's both a huge confirmation bias and huge self-selection bias. If I want to think of myself as an academic, I'll upload a picture of me at my desk studying and watch the "intellectual" ratings pour in. Then I can feel assured that other people perceive me the way I want to be perceived. Additionally, if one wants to conform to social expectations (and things like Asch's line test http://en.wikipedia.org/wiki/Asch_conformity_experiments indicate conformity is common), this data might really be nothing more than showing the degree to which people post photos affirming their conformity to their social expectations (i.e. 'smart' ethnicities posting 'smart-looking' photos) and be saying nothing at all about how people actually perceive ethic cues.
There are huge methodological concerns for this 'study'. Instead, the revelation of this data might actually be the insight that "pictures of yourself at social events makes you look more social." Taking much of anything at all away from this data set would be rather unwise.