Live Tweets of Girl:Guy Ratios in San Francisco
twitter.com
twitter.com
You download the app. Every night, the app serves up ONE bar or club with a little description, pictures, reviews etc. The list is curated (and later down the line, businesses could pay for the privilege of being featured). The secret trick, though, is that there are actually two bars selected every night. For one, it gets 60% of the females and 40% of the males, for the other one, it gets 60% of the males and 40% of the females. There is no way to know which group you've been selected into every night. For many nights, though, you will be at the bar where there is a good ratio of people you're interested in (assuming a largely hetero or bi population), which will encourage you to open the app every night (variable reward, a la slot machines).
Of course, getting your first downloads will be tough, but that is a separate problem. Thoughts?
The tweets are mainly that we're objectifying women. Eg. "this is beyond commodity and into unitless essence. ratio is how we speak of % nitrogen in the air", "Treating potential partners as a commodity demeans them and encourages objectification."
Our intention here isn't to objectify women (or men). We've just noticed pretty skewed ratios here in SF, and were curious about starting to report them. We even started offering some insight/analysis based on SF Census numbers.
We asked a few of our friends. Many of them, both m and f, gave us really positive feedback.
We're not trying to treat people like numbers. But, if you're trying to meet someone, it might be relevant. Of course, meeting people in real life and connecting with them on a deeper level is still completely a human endeavor.
What do you think?
A better system might be to scrape foursquare, twitter, facebook checkins, and just measure the ratios. It would be more automatic (no behavior change required), but would also be skewed (say, for example, women tweet more than men do).
The reason we decided against scraping these other sites is that few people actually check-in. In a full bar, it'd be optimistic for >5 people to check in. This makes ratios very inaccurate. There actually is a site out there based on Foursquare check-ins and they suffer from this problem.
I can see the incentive for disinformation, especially for managers/employees of these venues. This problem can be solved with scale (one reported ratio completely different from the others is a red flag). Of course until we reach scale, we can't do that; but also, until we reach scale, there is not really incentive to misreport.