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deed zacatecas aguascalientes was the site of some of the revolution s most brutal fighting and was thus a prime source of origin for early 20th century mexican immigrants also according to wikipedia san luis potosi which is right next door to zacatecas is home to a non trivial number of italians this might explain why my southern euro ancestry has an italian component cross state dalliances on the same italian note ancestry provides you with cousin matches out to the sixth degree for people who have also taken the test and appear to be related to you popping up in my matches are several people from the italo mexican region of san luis potosi fourth and fifth cousins extremely high probability only one of them has a picture i won t post it here but she looks very italian not at all mestizo there s some african and middle eastern noise which if legit certainly comes from my mother s side on average again per analabha basu et al mexicans exhibit a small amount roughly 4 of african ancestry a legacy of slavery sur de la frontera makes sense that a tiny amount would end up in my maternal lineage the middle eastern trace is probably a pulse from the old world je suis charles martel it s possible the m e trace could have sperm ed or egg ed its way into my lineage in mexico but since lebanese and other arabs didn t start arriving in mexico until the late 19th century i doubt that s the case raw data this is just the beginning i ve downloaded my 700 000 snps and indels and am looking forward to uploading the data to other tools to match against other databases i ll also be looking for ways these genetic testing algorithms might be valuable for analysis of large textual data sets posted in uncategorized 1 comment may 11 2016 by seth long relinquishing control responding to allington et al s argument that the digital humanities are a handmaiden to neoliberalism and non progressive scholarship j uliana spahr richard so and andrew piper respond that dh and progressive scholarship are not in fact incommensurable without getting too deep into the many contours of the debate i want to suggest in this post what i think may be the hidden crux of the argument though i doubt the authors of either essay would agree with me spahr et al ultimately what has most troubled us about allington et al s essay is its final line which is its core assertion they call on colleagues in the humanities to resist the rise of the digital humanities they have carefully studied the field of the digital humanities and declare that it must be shut down nothing good can come from it we worry about this foreclosing of possibility other academic disciplines such as sociology have benefited greatly from the merging of critical and computational modes of analysis particularly in overturning entrenched notions of gender or racial difference based on subjective bias we find it is too early to reject in toto the use of digital methods for the humanities the urgent questions articulated by neoliberal tools thus present a rich opportunity to think about the field s methodological potential questions about the over representation of white men or the disproportionate lack of politically progressive scholarship in the digital humanities regard inequality and have a strong empirical basis as such they cannot be fully answered using the critical toolbox of current humanistic scholarship these concerns are potentially measurable and in measuring them the full immensity of their impact becomes increasingly discernable and thus answerable the informed and critical use of quantitative and computational analysis would thus be one way to add to the disciplinary critique that the authors themselves wish to see in these final paragraphs the authors make the move an almost imperceptible move but i think i can detect it that anyone in the hard or social sciences must also make they separate data from explanations for data this in my view is what makes the ostensibly progressive or activist goals of some humanities scholarship somewhat incommensurable with computational work as such questions about equality the authors note are questions that require large scale measurement they are not questions one can address adequately through close readings or selective anecdotes which they describe as the critical toolbox of current humanistic scholarship what they do not note but i think it s a point allington et al might eventually get around to making in a counter argument is that when you exchange a close humanistic analysis for a data driven one then to a certain extent you relinquish control over the correct way to explain or theorize the resultant measurements indeed our results we now recognize are far too easily rationalized with just so stories that fit our pre conceived notions which isn t to say that some just so stories aren t also true stories or that some just so stories aren t truer than others the data s the data a biologist 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 ...
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