The Marriage Problem
The Loan Shark
The discussion of the marriage problem got me thinking about other contexts in which it may or may not be appropriate to bring up gender in a classroom. A friend asked his students a question about violent loan sharks (the knee cap breaking kind) and incentives for repayment of small loans. As the class was about microcredit, where most of the participants are female, and from a general cultural practice in this department to use the female pronoun for problems, my friend used she to refer to the lendee. Upon showing this problem to a colleague, the colleague suggested that my friend change the gender of the pronoun used, under the general principal that, as stated, the problem evoked violence against women in a way that was possibly distasteful. Since part of the point of this problem was to examine the societal benefits and detriments of the proposed loan shark situation, my friend let the wording stand. Unlike the previous situation, the use of gender in this context is not completely gratuitous. The reality of microcredit often is that women take on the responsibility and the associated risks of the loans they take out for their families, though the potential violence faced in microcredit is different than that of the loan shark situation described, who would probably have a mostly male clientele. It makes me wonder.
The New York Times
My partner found an interesting blog post attempting to study how men and women are treated in the New York Times. It looks at the relative frequency of words used in sentences about men versus women in the New York Times, during the week of Feb 27-Mar 6, 2013. (The full description of the methodology, as well as parts of the code used to do the analysis can be found in the post.) The results arefascinating disturbing unsurprising.
If you are an economist, or computer scientist, or in a related field, you probably know this problem, possibly under the less loaded title of Stable Matching Problem. If you don't know this problem, and don't want to read the wikipedia articles linked, this is a problem of having two sets of n (possibly infinite) elements, that one wants to match. Each element in each set has a set of preferences of which element of the opposite set it is matched to. The problem is to find a matching that optimizes along the predefined preferences. In terms of marriage, one wants to find a way of marrying the people in the two sets off, so that there are no two people who would rather be married to each other than to the person they are married to, thus resulting in stable marriages with minimal risk of infidelity. The criterion for solutions to this problem existing is fairly well known at this point. Finding them efficiently, I understand, may still be tricky. In particular, I believe the problem becomes much easier if there is one element that is indifferent towards (or equally happy with) all the elements of the other set.
A friend of a friend of mine (yes, this story is being told third hand, sorry) was sitting in a class at University F where the marriage problem was being discussed in the local language. When the lecturer turned to the case of an element with an indifferent set of preferences, he described her as the bitch attracted to all the men, switching to English for the single word. In retrospect, I suppose it could have been worse. He could have decided to call the element the slut. This got me thinking. If I put on my man-hating hat for a moment, and decide that this indifferent element is male, what would I call him? Mormon or Muslim come to mind, but I don't want to insult religions that allow for one-sided polygamy for the purposes of this exercise. Serial rapist, I suppose, but that is a classification of someone convicted (or at least accused) of a crime, not a casual insult. Don Juan has a positive connotation to it, as does player. I am again shocked at how surprisingly hard it is to condemn a man for the same sexual behavior that would easily get a woman labeled as slut or bitch, or worse. English just doesn't have the words. Any ideas?
The Loan Shark
The discussion of the marriage problem got me thinking about other contexts in which it may or may not be appropriate to bring up gender in a classroom. A friend asked his students a question about violent loan sharks (the knee cap breaking kind) and incentives for repayment of small loans. As the class was about microcredit, where most of the participants are female, and from a general cultural practice in this department to use the female pronoun for problems, my friend used she to refer to the lendee. Upon showing this problem to a colleague, the colleague suggested that my friend change the gender of the pronoun used, under the general principal that, as stated, the problem evoked violence against women in a way that was possibly distasteful. Since part of the point of this problem was to examine the societal benefits and detriments of the proposed loan shark situation, my friend let the wording stand. Unlike the previous situation, the use of gender in this context is not completely gratuitous. The reality of microcredit often is that women take on the responsibility and the associated risks of the loans they take out for their families, though the potential violence faced in microcredit is different than that of the loan shark situation described, who would probably have a mostly male clientele. It makes me wonder.
The New York Times
My partner found an interesting blog post attempting to study how men and women are treated in the New York Times. It looks at the relative frequency of words used in sentences about men versus women in the New York Times, during the week of Feb 27-Mar 6, 2013. (The full description of the methodology, as well as parts of the code used to do the analysis can be found in the post.) The results are
Male words Ratio Male Female Word 11.2 72 02 prime 10.8 70 02 baseball 9.5 92 03 official 9.5 61 02 capital 9.5 61 02 governor 5.8 75 04 fans 5.3 120 07 minister 5.3 51 03 sequester 5.2 118 07 league 4.5 58 04 failed 4.4 57 04 cardinals 4.2 54 04 finance 4.0 78 06 reporters 3.9 50 04 winning 3.8 73 06 finally 3.6 116 10 players 3.5 56 05 acknowledged 3.5 67 06 address 3.4 66 06 attack 3.3 108 10 opposition 3.3 54 05 rest 3.3 53 05 camp 3.2 52 05 costs 3.1 91 09 goal 3.1 50 05 crowd 3.0 118 12 bank 2.9 57 06 referring 2.9 66 07 sports 2.9 56 06 surgery 2.9 56 06 missed 2.8 55 06 pressure 2.8 64 07 teammates 2.8 91 10 economy 2.8 54 06 release 2.7 123 14 pope 2.7 130 15 meeting 2.6 84 10 victory 2.6 58 07 veteran 2.5 226 28 political 2.5 104 13 spending 2.5 64 08 effect 2.5 56 07 spend 2.5 72 09 continue 2.5 95 12 foreign 2.4 71 09 injury 2.4 94 12 election 2.4 78 10 running 2.4 116 15 manager 2.4 54 07 elected 2.4 99 13 tax Female words Ratio Male Female Word 100.0 0 29 pregnant 100.0 0 17 husband's 51.6 1 16 suffrage 40.3 2 25 breast 12.9 4 16 gender 11.8 6 22 pregnancy 6.8 10 21 dresses 5.7 13 23 birth 5.5 13 22 memoir 4.8 25 37 baby 4.7 17 25 disease 4.6 14 20 interviewed 4.6 12 17 abortion 4.6 24 34 dress 4.5 23 32 married 4.3 12 16 activist 4.3 25 33 author 4.1 14 18 drama 3.9 30 36 hair 3.8 18 21 rape 3.6 24 27 dog 3.6 19 21 novel 3.5 99 108 children 3.4 16 17 statue 3.4 17 18 victim 3.4 51 53 cancer 3.3 41 42 violence 3.2 32 32 younger 3.2 20 20 festival 3.1 34 33 study 3.1 30 29 teacher 3.1 27 26 sex 3.1 43 41 fashion 3.1 20 19 opera 3.0 18 17 singing 3.0 62 57 child 2.8 23 20 wear 2.8 30 26 native 2.6 34 27 dance 2.6 29 23 graduated 2.5 33 26 writer 2.5 23 18 favor 2.5 41 32 eyes 2.5 22 17 becomes 2.5 47 36 kids 2.5 21 16 eat 2.4 29 22 domestic 2.4 29 22 traditional 2.4 77 58 parents 2.4 32 24 drugThe author sums it up best:
If your knowledge of men's and women's roles in society came just from reading last week's New York Times, you would think that men play sports and run the government. Women do feminine and domestic things.As someone who used to read the NYTimes a lot, I'm not shocked by this revelation. Our brains (or at least my brain) pick up on these messages subconsciously. Our brains also pick up on a lot of subconscious messages that aren't actually there, or at least not supportable by the data. It's good to see this backed by numbers.
