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Measuring representation in media
destntoast wrote in fandomstats
[also posted something similar about this on Tumblr; I still am not sure whether this LJ community is sustainable when people are mostly on Tumblr these days... but I do find threaded conversations a lot easier here.]

I'm interested in trying to assess the degree of representation in media -- of gender, initially, but if I can eventually figure out how to assess other representation (POC, queer characters or actors, religion, country of origin, etc.) that would also be awesome.  In part, I'd like to try to quantify disparities in modern popular media. But from a fandom perspective, I'm also really interested in how this ties into shipping.  How much of the predominance of M/M and lack of F/F in fandom is explained by the gender representation (or lack thereof) in the source material?

Figuring out how to get representation data about source media is tricky.  If I stick to TV and/or movies, IMDB at least lists what actors appear in, and the actors' genders.  I can look at billing order or number of episodes that way.  Maybe there are also other good metrics I should pay attention to?

Better still would be to measure screen time or lines of dialogue. I did an analysis for Sherlock of the number of lines of dialogue each character has based on arianedevere's transcripts, but that’s too time intensive to do for a large batch of shows (since there’s no central repository of transcripts that are all formatted the same way).

These are some numbers I got when horsing around with various different methods of measuring representation from the data on IMDB:


  1. I could compare the number of actors of different genders who were in at least X of the episodes -- e.g.,
    in BtVS, there are 8 women and 8 men who are in at least 20/145 episodes;
    in Xena, there are 4 women and 4 men who are in at least 10/134 episodes;

    in Sherlock, there are 4 women and 6 men who are in at least 5/15 episodes (note that as well as the 9 main episodes, IMDB also counts Unaired Pilot, Many Happy Returns, the Christmas Special, and the 3 episodes of s4 based on rumored casting so far)

  2. I could compare the total number of appearances by all male actors to total number of appearances by all female actors (again excluding minor guest roles) -- e.g.,
    in BtVS, there are 623 appearances by women and 551 appearances by men (of characters who appeared in at least 20 episodes);
    in Xena, there are 290 appearances by women and 107 appearances by men (of characters who appeared in at least 10 episodes);
    in Sherlock, there are 32 appearances by women and 64 appearances by men (of characters who appeared in at least 5 episodes)

  3. As I said, lines of dialogue is too labor intensive to do on a big scale -- but just by way of comparison for Sherlock:
    in Sherlock, there are 1K lines of dialogue by women and 7.5K lines of dialogue by men (of characters with at least 50 lines)



(Sorry for the inconsistent thresholds; I was just playing around and eyeballing things.  I’d have to figure out a more consistent threshold to scale this up.)

Out of the two feasible options above, #2 looks to me to be closer to how I would assess the representation of the two shows -- though it still substantially overestimates the amount of female representation in Sherlock, in terms of actual screen time.  (And I would prefer to weed out the unaired episodes, if I did this again.)

Comparing to method #3 points out how severely the other methods can underestimate the screen time allocated to just a few characters in a show like Sherlock, which is dominated by two characters -- probably the same is true for Xena.   One might be able to mitigate this somewhat by finding optimal thresholds for number or percentage of episodes -- and/or by looking to see whether there are a small number of characters who are in far more episodes than everyone else.  But there just might not be a great quick metric that accurately takes into account screen time.

However, screen appearances may still be a reasonable way of trying to roughly tell how many characters are likely to register as shippable on the fandom radar.  The above gender ratios do seem to roughly predict the amount of M/M, F/F, and F/M in each of the fandoms.

Suggestions for how best to measure gender representation in TV shows from easily available online data (e.g., IMDB or Wikipedia) are welcome! Also for how to measure other representation -- POC, queer characters or actors -- but I suspect that’s much harder. :-/

Any ideas?  Thoughts?  Warnings?  Pointers to related work?  :)

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Oooooh! So many good points. Thank you!

I do think that some factors would be hard to quantify (e.g., UST; number and type of canonical pairings -- not sure how to count that, especially assuming there's some churn; pairings that fans would consider taboo or squicky for various reasons). Some things, like the characters being reasonable ages to ship together, and not having the same last name, one could theoretically probably account for based on data available about the characters and actors. Really iteresting to think about how to get at all of these, though!

For my current analysis, I'm probably going to stick to trying to find relatively quick and dirty heuristics to get a sense of gender representation on various shows, and hope that by having a whole bunch of data points, I can use that to start to get a sense of what fraction of "why so much M/M slash?" can be explained by the source media. But it'll be a rough estimate that doesn't account for a bunch of this stuff.

Your questions about audiences and why people consume particular things is another interesting one. I've definitely seen in fanfic that preferences in production and preferences in consumption are not always the same. Hmmm... good stuff to chew on! (I don't feel discouraged; thanks! :) )

I think a rough estimate will certainly be useful to the discussion, and I look forward to seeing your results! (though I have a pretty good guess as to how they'll turn out...)

Yeah, I'm quite sure there will be a correlation, but I'm not sure how much of the variance will be explained by the numbers. It'll be fun to see! :)

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