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Text of the page (random words):
st friend of mine once said it s how you explain the data that gives rise to debates take ted underwood s piece on gender representation in fiction which spahr et al point to as an example of critical computational scholarship underwood writes that between 1800 and 1989 the words associated with male vs female characters are volatile and in fact become more volatile in the twentieth century making it more difficult for models to predict whether a set of words is being applied to a male or a female gender he concludes is not at all the same thing in 1980 that it was in 1840 ah gender is fluid we might conclude solid computational evidence for feminist theory but then underwood makes the data grounded move noting that cause s of the trend are open to interpretation and further data exploration the convergence of all these lines on the right side of the graph helps explain why our models find gender harder and harder to predict many of the words you might use to predict it are becoming less common or becoming more evenly balanced between men and women the graphs we ve presented here don t yet distinguish those two sorts of change whether previously gendered terms converge toward both male and female characters or whether gender predicting terms simply disappear in fiction could very much make a difference from the standpoint of explanation especially critical or political explanation e g one could claim given the latter case disappearance of gender predicting terms that what we see at work is the ignoring of gender rather than the fluid reframing of it an effect say of feminism on fiction but not in any sense a confirmation of the essential fluidity of gender however it would also be perfectly feasible to use either explanation to forward a more critical or activist minded thesis it could go either way and there s the rub when you re doing computational work you cannot also at the same time be explaining your results explanation is step two and it s a step people can take in different directions politically friendly politically unfriendly or politically neutral and if the computational work you re doing is interesting you should at least sometimes find things that overturn your preconceived notions for example underwood notes that despite the general trend away from sharply delineated gender descriptions there are some important counter trends on balance that s the prevailing trend but there are also a few implicitly gendered forms of description that do increase in particular physical description becomes more important in fiction heuser and le khac 2012 and as writers spend more time describing their characters physically some aspects of the body and dress also become more important as signifiers of gender this isn t a simple monolithic process there are parts of the body whose significance seems to peak at a certain date and then level off like the masculine jaw maybe peaking around 1950 other signifiers of masculinity like the chest and incidentally pockets continue to become more and more important for women the eyes and face peak very markedly around 1890 but hair has rarely been more gendered or bigger than it was in the 1980s rethinking things perhaps we don t see evidence that gender is fluid so much as evidence that gender remains sharply delineated just along a different terminological axis than was previously the case or not you could argue something else too again that s the point as another example of what i m talking about we can look at juliana spahr s and stephanie young s work on the demographics of mfa and english phd programs it is an excellent piece tied resolutely to statistics but it ends this way we have ended this article many different ways made various arguments about what is or what might be done these arguments now seem either inadequate reformist or unrealistic smash the mfa the awp the private foundations the state at moments we struggled with our own structural positions even as these structures were created without our consent but to our advantage we agree with mcgurl when he argues that w hat is needed now are studies that take the rise and spread of the creative writing program not as an occasion for praise or lamentation but as an established fact in need of historical interpretation how why and to what end has the writing program reorganized u s literary production in the postwar period for us for now the best we can do is work to understand so that when we create alternatives to the program they do not amplify its hierarchies more research needed in other words any previous calls to activism muted spahr and young do a wonderful job compiling relevant demographic information but in so doing they rightly recognize that interpreting the information both historically and in the present moment is another job altogether the data are separated from their explanation spahr and young are i imagine on the political left but their data remain open to explanation from multiple political or apolitical perspectives from an apolitical perspective i would want to explain some of their demographic data with simple demography for example they imply that 29 non white representation in english phd programs is not enough but america is precisely 29 non white and 71 white so i don t find that statistic problematic at all i would also claim that this same demographic point partially ameliorates the 18 non white representation in mfa programs though obviously a gap in representation remains how to explain it though their essay is rightly not ideological enough to foreclose on all but a single left facing window of possibility this is a good thing recognizing the possibility of multiple explanations is what keeps a field of inquiry from becoming an ideological echo chamber spahr et al also point to sociology as a field that uses computational methods to address critical cultural questions but again addressing critical or cultural questions with computational methods is not at all the same thing as being critical culturally progressive or activist sociologists and psychologists have i think always recognized if only quietly that progressive or activist readings of their data are by no means the only readings steven pinker and jon haidt among others are really pushing the point lately with their heterodox academy it s all a big debate of course but that s the point in my view good computational scholarship opens up debate and rarely points to one single and obvious and you re stupid if you don t believe it conclusion sometimes it does but that s usually in the context of not immediately political content e g whether or not piraha possesses recursive syntax but when you re talking about large social or political explanations i ve never seen the explanation that doesn t leave me thinking mm maybe interesting i dunno we ll see i m sure my skepticism comes across as conservatism to some from my perspective as a scholar however i m simply tentative about my own worldview i m therefore deeply suspicious of any scholar or study purporting to provide 100 support for any particular ideology or political platform so i think it s a good thing that a lot of dh work doesn t do that indeed i m drawn most often to theories that piss off everyone across the political spectrum e g gregory clark s work because my most deeply held prior is that the world as it is probably won t conform very often to any particular ideology or politics if anything then i d like to see more dh work not confirming a single orthodoxy but challenging many orthodoxies all at once then i ll be confident it s doing something right posted in uncategorized leave a comment march 30 2016 by seth long readability formulas readability scores were originally developed to assist primary and secondary educators in choosing texts appropriate for particular ages and grade levels they were then picked up by industry and the military as tools to ensure that technical documentation written in house was not overly difficult and could be understood by the general public or by soldiers without formal schooling there are many readability metrics nearly all of them calculate some combination of characters syllables words and sentences most perform the calculation on an entire text or a section of a text a few like the lexile formula compare individual texts to scores from a larger corpus of texts to predict a readability level the most popular readability formulas are the flesch and flesch kincaid flesch readability formula flesch kincaid grade level formula the flesch readability formula last chapter in the link results in a score corresponding to reading ease difficulty counterintuitively higher scores correspond to easier texts and lower scores to harder texts the highest easiest possible score tops out around 120 but there is no lower bound to the score wikipedia provides examples of sentences that would result in scores of 100 and 500 the flesch kincaid grade level formula was produced for the navy and results in a grade level score which can be interpreted also as the number of years of education it would take to understand a text easily the score has a lower bound in negative territory and no upper bound though scores in the 13 20 range can be taken to indicate a college or graduate level grade so why am i talking about readability scores one way to understand distant reading within the digital humanities is to say that it is all about adopting mathematical or statistical operations found in the social natural physical or technical sciences and adapting them to the study of culturally relevant texts e g matthew jocker s use of the fourier transform to control for text length ted underwood s use of cosine similarity to compare topic models even topic models themselves which come out of information retrieval as do many of the methods used by distant readers these examples could be multiplied thus i m always on the lookout for new formulas and python codes that might be useful for studying literature and rhetoric readability scores it turns out have sometimes been used to study presidential rhetoric specifically they have been used as proxies for the intellectual quality of a president s speech writing most notably elvin t lim s the anti intellectual presidency applies the flesch and flesch kincaid formulas to inaugurals and states of the union discovering a marked decrease in the difficulty of these speeches from the 18th to the 21st centuries he argues that this decrease should be understood as part and parcel of a decreasing intellectualism in the white house more broadly ten seconds of googling turned up a nice little python library textstat that offers 6 readability formulas including flesch and flesch kincaid i applied these two formulas to the 8 spoken written sotu pairs i ve discussed in previous posts i also applied them to all spoken vs all written states of the union copied chronologically into two master files here are the results s spoken w written flesch readability scores for states of the union lower score more difficult flesch kincaid grade level scores for states of the union the obvious trend uncovered is that written states of the union are a bit more difficult to read than spoken ones contra rule et al 2015 this supports the thesis that medium matters when it comes to presidential address presidents simplify or as lim might say they dumb down their style when addressing the public directly they write in a more elevated style when delivering written messages directly to congress for the study of rhetoric then readability scores can be useful proxies for textual complexity it s certainly a useful proxy for my current project studying presidential rhetoric i imagine they could be useful to the study of literature as well particularly to the study of the literary public and literary economics does reading difficulty correspond with sales with popular vs unknown authors with canonical vs non canonical texts which genres are more difficult and which ones easier of course like all mathematical formula applied to culture readability scores have obvious limitations for one they were originally designed to gauge the readability of texts at the primary and secondary levels even when adapted by the military and industry they were meant to ensure that a text could be understood by people without college educations or even high school diplomas thus as begeny et al 2013 have pointed out these formulas tend to break down when applied to complex texts flesch kincaid grade level scores of 6 vs 10 may be meaningful but scores of say 19 vs 25 would not be so straightforward to interpret also like most nlp algorithms the formulas take as inputs things like characters syllables and sentences and are thus very sensitive to the vagaries of natural language and the influence of individual style steinbeck and hemingway aren t easy reads but because both authors tend to write in short sentences and monosyllabic dialogue their texts are often given scores indicating that 6th grades could read them no problem and authors who use a lot of semi colons in place of periods may return a more difficult readability score than they deserve since all of these algorithms equate long sentences with difficult reading however i imagine this issue could be easily dealt with by marking semi colons as sentence dividers all proxies have problems but that s never a reason not to use them i d be curious to know if literary scholars have already used readability scores in their studies they re relatively new to me though so i look forward to finding new uses for them posted in uncategorized 1 comment march 28 2016 by seth long cosine similarity parameters tf idf or boolean in a previous post i used cosine similarity a vector space model to compare spoken vs written states of the union in this post i want to see whether and to what extent different metrics entered into the vectors either a boolean entry or a tf idf score change the results first here s a brief recap of cosine similarity one way to quantify the similarity between texts is to turn them into term document matrices with each row representing one of the texts and each column representing every word that appears in both of the texts the matrices will be sparse because each text contains only some of the words across both texts with these matrices in hand it is a straightforward mathematical operation to treat them as vectors in euclidean space and calculate their cosine similarity with the euclidean dot product formula which returns a metric between 0 and 1 where 0 no words shared and 1 exact copies of the same text but what exactly goes into the vectors in these matrices not words from the two texts under comparison obviously but numeric representations of the words the problem is that there are different ways to represent words as numbers and it s never clear which is the best way when it comes to vector space modeling i have s...
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