A Brief Analysis of Text and ASCII Code

To verify that the Lexos hierarchical clustering returns an identical dendrogram whether, in text or in ASCII code, I first cleaned Nathaniel Hawthorne's Blithedale Romance leaving the capital letters as is. I then converted each letter into its ASCII equivalent and then wrote the lines to a new file. I then had two files.

The text file looks like this:

I OLD MOODIE The evening before my departure for Blithedale I was returning to my bachelor apartments after attending the wonderful exhibition of the Veiled Lady when an elderly man of rather shabby appearance met me in an obscure part of the street of my story The reader therefore since I have disclosed so much is entitled to this one word more As I write it he will charitably suppose me to blush and turn away my face I I myself was in love with Priscilla

And, the ascii "out-1.txt file looks like this:

073 079076068 077079079068073069 084104101 101118101110105110103 098101102111114101 109121 100101112097114116117114101 102111114 066108105116104101100097108101 073 119097115 114101116117114110105110103 116111 109121 098097099104101108111114 097112097114116109101110116115 097102116101114 097116116101110100105110103 116104101 119111110100101114102117108 101120104105098105116105111110 111102 116104101 086101105108101100 076097100121 119104101110 097110 101108100101114108121 109097110 111102 114097116104101114 097 115104097098098121 097112112101097114097110099101 109101116 109101 105110 097110 111098115099117114101 112097114116 111102 116104101 115116114101101116 111102 109121 115116111114121 084104101 114101097100101114 116104101114101102111114101 115105110099101 073 104097118101 100105115099108111115101100 115111 109117099104 105115 101110116105116108101100 116111 116104105115 111110101 119111114100 109111114101 065115 073 119114105116101 105116 104101 119105108108 099104097114105116097098108121 115117112112111115101 109101 116111 098108117115104 097110100 116117114110 097119097121 109121 102097099101 073 073 109121115101108102 119097115 105110 108111118101 119105116104 080114105115099105108108097

Both files are much longer than the above samples. The resulting dendrogram from the default settings of Lexos's hierarchical clustering looks like the image below. Heuristically the computer program of Lexos interprets each file exactly the same, and yet each file is very different. The slightly obtuse python code that I used to create the ASCII file and the text file is below. I can make it much prettier, but for now, it is what it is.

\#!/usr/bin/env python3
\# -*- coding: utf-8 -*-
"""
Created on Sun Apr  9 08:49:53 2017

@author: ray

"""
\# An alternative to replacing brackets and parentheses by using regex within python
def remove_bracketed_text_by_regex(text):
   import re
\#    text = re.sub("\(.+?\)", " ", text)         # Remove text between parentheses
\#    text = re.sub("\[.+?\]", " ", text)         # Remove text between square brackets
\#    text = re.sub("\s+", "  ", text).strip() # Remove extra white spaces (optional)
   return text

\# A loop subroutine def that will remove nested brackets and parentheses
def remove_text_inside_brackets(text, brackets="()[]"):
   count = [0] * (len(brackets) // 2) # count open/close brackets
   saved_chars = []
   for character in text:
       for i, b in enumerate(brackets):
           if character == b: # found bracket
               kind, is_close = divmod(i, 2)
               count[kind] += (-1)**is_close # `+1`: open, `-1`: close
               if count[kind] < 0: # unbalanced bracket
                   count[kind] = 0
               break
       else: # character is not a bracket
           if not any(count): # outside brackets
               saved_chars.append(character)
   return ''.join(saved_chars)

\# the cleanstring subroutine def below substitutes one character for another or for nothing if the second quote is left empty. Modify as needed
def cleanString(incomingString):
   newstring = incomingString
   newstring = newstring.replace("a","097")
   newstring = newstring.replace("A","065")
   newstring = newstring.replace("b","098")
   newstring = newstring.replace("B","066")
   newstring = newstring.replace("c","099")
   newstring = newstring.replace("C","067")
   newstring = newstring.replace("d","100")
   newstring = newstring.replace("D","068")
   newstring = newstring.replace("e","101")
   newstring = newstring.replace("E","069")
   newstring = newstring.replace("f","102")
   newstring = newstring.replace("F","070")
   newstring = newstring.replace("g","103")
   newstring = newstring.replace("G","071")
   newstring = newstring.replace("h","104")
   newstring = newstring.replace("H","072")
   newstring = newstring.replace("i","105")
   newstring = newstring.replace("I","073")
   newstring = newstring.replace("j","106")
   newstring = newstring.replace("J","074")
   newstring = newstring.replace("k","107")
   newstring = newstring.replace("K","075")
   newstring = newstring.replace("l","108")
   newstring = newstring.replace("L","076")
   newstring = newstring.replace("m","109")
   newstring = newstring.replace("M","077")
   newstring = newstring.replace("n","110")
   newstring = newstring.replace("N","078")
   newstring = newstring.replace("o","111")
   newstring = newstring.replace("O","079")
   newstring = newstring.replace("p","112")
   newstring = newstring.replace("P","080")
   newstring = newstring.replace("q","113")
   newstring = newstring.replace("Q","081")
   newstring = newstring.replace("r","114")
   newstring = newstring.replace("R","082")
   newstring = newstring.replace("s","115")
   newstring = newstring.replace("S","083")
   newstring = newstring.replace("t","116")
   newstring = newstring.replace("T","084")
   newstring = newstring.replace("u","117")
   newstring = newstring.replace("U","085")
   newstring = newstring.replace("v","118")
   newstring = newstring.replace("V","086")
   newstring = newstring.replace("w","119")
   newstring = newstring.replace("W","087")
   newstring = newstring.replace("x","120")
   newstring = newstring.replace("X","088")
   newstring = newstring.replace("y","121")
   newstring = newstring.replace("Y","089")
   newstring = newstring.replace("z","122")
   newstring = newstring.replace("Z","090")
   newstring = newstring.replace('\/',' ')
   newstring = newstring.replace('"',' ')
   newstring = newstring.replace('.', ' ')
   newstring = newstring.replace(',',' ')
\#    newstring = newstring.replace('\\n','')
   return newstring

f2 = open(r'C:\Users\rayst\Documents\525-DH\texts-for-analysis\ascii\output\Hawthorne-blithedale-romance--ascii-out-1.txt', "w") # open a new file to write to.
\#much of the stuff below is commented out and only there for convenience.
\# the following "for loop" runs the above subroutine defs
\# with open('commentarymagazine_humanities_urls.json', 'r', encoding='utf-8') as f:\n
\# on each line of text in the input file
\# When it reaches the end of file it breaks out of loop.
for line in open(r'C:\Users\rayst\Documents\525-DH\texts-for-analysis\ascii\Hawthorne-blithedale-romance-ascii.txt', "r"):
\# Uncomment the following four lines of code to remove from an asterisk to the end of line. Yeah, so, these are line operations.
\#    head, sep, tail = line.partition('*.')
\#    line = (head)
\#    head, sep, tail = line.partition('Lines')
\#    line = (head)
\# The following line of code removes nested brackets/parens within a line
   line = (repr(remove_text_inside_brackets(line)))
\# The commented line below offers an alternative to the above loop by using regex
\#   line = remove_bracketed_text_by_regex(line)
   line = (repr(cleanString(line))) # this calls the above cleanstring sub for each line
   line = line.replace("'"," ") # this gets rid of any remaining apostrophies
   line = line.replace('\\n',' ')
   line = line.replace('\\',' ')
   line = line.replace("\""," ") # this gets rid of any remaining commas
   line = line.replace('!',' ')
   line = line.replace('?',' ')
   line = line.replace('-',' ')
   line = line.replace(';',' ')
   line = remove_bracketed_text_by_regex(line)
   print(line) # this prints the output to the file in the console screen for monitoring
   f2.write(line) # this writes the line to the cleaned output file
\#    f2.write(r"\n") # this appends each line with a newline

f2.close() # this closes the output file

What I'm getting at is the bias in Topic Modeling and other Digital Humanities research. I'd like to develop a blind experiment to establish the extent to which Digital Humanities is objective. I've been thinking about this since objectivity came up in our WE1S Summer Research Camp discussions. I'm thinking about how to prove with hard evidence that DH is a science. I'm thinking about a mathematician, or geneticist, and a digital humanist arriving at the same technical observations of the outputs--the results produced by the DH science. Perhaps this has all been done before, but in any event, I proved to myself with a fun little experiment that the computer doesn't make choices based on what the tokens are. It processes the relationships (maybe not the order but probably the quantity) of the tokens to one another. The experiment verifies that this is so despite the underlying mathematical proof of hierarchical cluster modeling.

Does Hierarchical Clustering produce different results based on the order of the tokens? If I were a mathematician, I might believe it one way or another. But, this test helps me to visualize what is taking place. The numbers refer to the dendrogram and the word file as much as to any equivalent token set, and it doesn't depend on whether the equivalent token file is made from a 1 to 1 exchange of hexadecimal, binary or pictograph equivalents. I guess what is next needed is something like a diagram that expresses the flow of meaning, from our semiotic registers into the 1s and zeros and then back. What is taking place? Yes, an analysis, but where does that analysis lose those to whom we are advocating for the DH? Our entire world is being digitized and even our brains. What are we losing in the process and how can Digital Humanities help lessen the loss?

The same text to ascii test applied with an actual ascii to text online converter and the topic modeling tool for use that we downloaded gave me the following words from Topic 0 when I converted them from the ASCII coded file of Blithdale Romance: window drawing room hotel curtain front windows city extended dove steps All area places houses curtains boarding cat return doors

When I ran the topic modeling tool with the text version file Topic 0 came out as the following: boat wrong river effort borne act bent coffin drift shore shoe emotion sobs dry yonder methinks base tragedy betwixt tuft

Everything was the same, but likely the seed started at a different token. I need to check into that. Otherwise, I'll have to investigate why there is a difference in topic modeling the ASCII encoded equivalent of a text file.

Continuing on, I ran the topic model again with the same exact set of text files (I used Lexos to cut the files above into 9 segments and then downloaded them. Note that the download segemented files on the Lexos || prepare || cut menu did not seem to work for me so I had to download the cut files from the Manage menu), but this time I got the following list of topics for Topic 0: fauntleroy chamber wealth splendor daughter corner glass saloon drink moodie governor liquor wife condition supposed beautiful cocktails gin feeble message

This indicates to me that the topic modeling tool uses a different seed token to generate Topic 0. Because of the different Topic 0s produced whenever a different seed token is used, I have to question the validity of the topic modeling tool or look into what others say about this issue. If topics may relate to themes based on relative coherence then what I would like to see is an average Topic 0 made from the sums of analyses run from all possible seed tokens. I'd like to concentrate on other topic modeling variables instead of having such an open variable as part of my research.

The above tests helped me realize

1. the meanings of the words are equivalencies of human awareness that the world and life exist.
2. the meaning of the words from a novel written almost 200 years ago remains unchanged even when converted to another code. Humanly readable topics were determined after I switched the topic modeling results back from their ASCII codes to text.
3. the meaning of the words may be analyzed by processes outside the scope of the analyst's knowledge.
4. semantic relationships between words result from computer processes. The topics did seem to have a bit of coherence. On other topic models with rubust data sets, topics have suprisingly uncanny coherence.
5. the code being processed by another auxiliary code is the artificial processing of the awareness that the world and life exist.

On Tolkien, Menus, and Claves

On Tolkien, Menus, and Claves

“Tolkien’s Creation of the Impression of Depth” (2014) by Michael D. C. Drout, Namiko Hitotsubashi, Rachel Scavera reminded me of using Lexos to find record keeping sections of texts within medieval guild texts which were distinctly different than the prose and verse of a medieval compendium. Keeping “Tolkien’s Creation” in mind, how then did the record keeping texts give the overall compendium of texts depth? They did two things for us as novice researchers. First, they provided a level of credibility such that our minds realized for certain that this was a real history written by a scribe (as if in an office separated only by the distance of time?). Second, the level of authenticity associated to accounting and money transferred from the record-keeping texts to the romance novels that we were searching. It gave us a sense that the Middle Age words, even though foreign in their spelling and pronunciation, were words written by people that felt what the words meant.

This led me to question how we come to know anything new to us. Do we go into a new job as if it is a building full of mirrors; blank referents without anything except memories of our own to fill? Are we always walking through life with the mirrors of our previous knowledge that fill the spaces around us such that our preconceived ways prevent us from experiencing the new? Is the filling and refilling of referents a process amenable to modification? Can studying literature help us understand how we exchange our quasi-referents for what we will? What is my personal comfortable level between knowing and not knowing anything?

So, I wondered further about a book that I’m reading by Foucault: The Order of Things (1970). He prefaces it with a section that talks about a Borges passage wherein a “‘a certain’” Chinese encyclopedia’” in which it is written that ‘Animals are divided into (a) belonging to the Emperor, (b) embalmed, (c) tame, (d) sucking pigs, (e) sirens, (f) fabulous, (g) stray dogs, (h) included in the present classification, (i) frenzied, (j) innumerable, (k) drawn with very fine camelhair brush, (l) et cetera, (m) having just broken the water pitcher, (n) that from a long way off look like flies’” The classification by enumeration is the only thing that seems to make sense. But, the heterogeneity of the disassociated referents listed produces a “‘loss to what is “common” to place and name” (xix). And further according to the author, “heterotopias [such as this Borges quote] desiccate speech, stop words in their tracks, contest the very possibility of grammar at its source; they dissolve our myths and sterilize the lyricism of our sentences” (xviii).

Whereas, according to the authors of “Tolkien’s Creation,” “[t]raditional referents are thus often efficient ways of solving problems of the interlinking of form and content” (174), the text of Borges appears at first a categorization preventing referents from connecting to one another. Yet there is the reference to a Chinese dictionary that lists things as such. Therefore, the narrator references China as culturally similar to the above categorization. The point that I want to make about reading “Tolkien’s Creation of the Impression of Depth” is that Borges’s quote rests at a value related to how close a referent may be to a traditional referent. In Borges’s case, the referents listed weren’t merely broken, they dissolve or prevent “efficient ways of solving problems of interlinking form and content.” This made me want’ to add the term of anti-referent to the various types of referents. Or, in other words, anti-referents would be referents that have a value of absolute resistance to becoming referents; proportional to the unorderly way the images collate in the mind. To me, then Borges establishes a cultural view of China as a place that is beyond categorization in our western sense of order: it is culturally anti-referent to the way we understand things. As Foucault says, “There would appear to be, then, at the other extremity of the earth we inhabit, a culture entirely devoted to the ordering of space, but one that does not distribute the multiplicity of existing into any of the categories that make it possible for us to name, speak, and think” (xix).

This made me think of the first essay that I read for this week "Against Cleaning" and Anna Lowenhaupt Tsing’s "On Nonscalability." So what can be determined to be scalable, or like the way I’m thinking, put into an automated process such as Lexos? Could a program or Lexos locate within Borges’s texts the heterotopias or for that matter the menus that are nonscalable in “Against Cleaning?” Would these things show up as lexomic claves?
In “Tolkien’s Creation” the authors noted what they determined by typical literary analysis, things such as stylistic differences, poetic interpolation, pseudo-references, broken references, “anaphora, alliteration, rhyme, polysyndeton, parataxis octosyllabic rhyming couplets, and repetition . . . There are passages, short strings of sentences, individual sentences or even single clauses which read as if they were poetry adapted to prose” (184-85). How much of critical literary research may be automated? Were the authors suggesting that the effective use of lexomics and to a large part Digital Humanities is for determining authorship and wildly differing portions of text rather than the subtleties of literary analysis? I know better, but since the authors never mentioned exactly what machine learning could and couldn’t do I became suspect.

Processing massive amounts of texts warrant more specialized programming such as in the “Against Cleaning” menu project, whereas the study conducted in “Tolkien’s Creation” warranted the processing by the Lexos pre-existing software for a single particular purpose. The authors made an appropriate choice of machine learning and standard critical literary analysis. The nuances of style related to a research project, versus the effort to write or find software that could analyze what you are looking for is always a compromise. Without being explicitly programmed, machine learning produced statistical analysis appropriate to the way the authors of “Tolkien’s Creation” achieved their goals. Each DH project must be evaluated according to its needs.

Here is the Dendrogram that shows the accounting/record-keeping in the latter sections of Common Place Books when compared with Canterbury Tales.

Different Ways We Get to Here

S. Ramsay

"Here” is the finished literary article, story, or as in the case of Stephen Ramsay's essay "The Hermeneutics of Screwing Around" (2010), the research--the incentive to search. Ramsay mentions two ways to get here, the first being "some coherent, authoritative path through what is known" (1), and the second, screwing around browsing the web while letting the mind hop connected neuronal link (web page) to neuronal link (web page) to some kind of hyper-realization that this is what I've been looking for. One is pragmatic while the other is anarchic. Each has its benefits. I remember Derrida saying something like (air quotes) he believes formal education is necessary but should be forgotten (air quotes). What I'm getting at is that we need a pragmatic way to establish a base from which to launch our missiles of individuality. The conformity to standards "'a science, a method, a research, a pedagogy'" gives us a way to as the author says, to "understand each path through the vast archive as an important moment in the world's duration--as an invitation to community, relationships, and play" (Ramsay 2010, 9). These "paths" are themes and genres, possibly obscure, but connected streams of information that pass through territories of research materials to supply the common elements and minerals of our analyses. It is the job of [Topic modeling] in the Digital Humanities to first comprise an archive of texts (corpus) and then connect the paths into a map of the topic model to see which themes (topics) we are interested in. With a base built from the topic modeler we have a choice and freedom to go wherever our individuality takes us. Nonetheless, this still leaves us with the aesthetic issue of human creativity which some think may be lost through topic modeling.

S. Marche

Unlike Stephen Marche's essay "Literature Is not Data: Against Digital Humanities" (2012) wherein the author claims that "Literature cannot meaningfully be treated as data," I find the connections in the data that Digital Humanities provides to be of prime importance. Only after reading Lisa Samuels and Jerome McGann's essay "Deformance and Interpretation" (1999), a kind of precursor to Digital Humanities did I feel that I had a somewhat scientific knowledge of what poetry is--that sensation of something more, beyond the hint that language is capable of. In their treatment of poetry the author's state:

L. Samuels

J. McGann

It can be the sound the syllable makes in the spoken version of its written production—the life of its print, the sign of the imperative that the marks of printed language are only one part of a language event also spoken. The syllable of a syllable can also be the letters which are the smallest units of any syllable, the shifting territory between and alongside phonemes and morphemes, as well as phonemes and morphemes themselves. It can also be the idea of the syllable, the Platonic syllable’s “signified.” Stevens’s phrase, as we grope to explain it, to paraphrase it, emerges as an image of something we do not know. The insight into the aesthetic value of the results produced by Digital Humanities never leaves the mind of the researcher. Through "technical" deformance of poems, we as literary students gain a better knowledge of literary forms.

S. Selisker

H. S. Syme

The two critiques of Marche’s essay by Scott Selisker, “The Digital Humanities?,” and Holger Schott Syme, “Imaginary Targets” explain in detail the logical problems with the essay and go on to talk about how Digital Humanities helps solve “literary--historical problems.” To me, Digital Humanities treats data as “facts and statistics collected together for reference or analysis;” collected from texts, as information, as elements--tokens--words separated by spaces,--drawn from digital streams of 1s and 0s to allow us to search at the periphery of what “emerges as an image of something we do not know:” DH allows us to study how meaning manifests and to further examine the reason, as Marche puts it, “why words seem to mean so much more than they mean.” “What Happens When an Algorithm Helps Write Science Fiction” (2017) by Stephen Marche documents an attempt to write a story with words that mean more than they mean. Contrary to the author’s implied claim five years earlier that DH algorithms can’t achieve the status as producer of aesthetic value, within this essay he states he is searching for a “technology that can make [him] better at [his] job [as a writer of science fiction]”--a Digital Humanities technology. And further, he takes his editor’s comment of “‘the fact that it’s [the story written with such technology by him] not that bad is kind of remarkable,’’’ as a compliment.

Obviously, the author had a change of heart after more closely working with Digital Humanities. I think this is what happens to most literary scholars when they delve into DH. First, they have an aversion to it because of the technical barriers, and then they see that the secret they sought in the humanities might be better detailed by using Digital Humanities. Then, they find they are "here," on a map, at the front advocating for Digital Humanities.

Bibliography

.	Marche, Stephen. n.d. “Literature Is Not Data: Against Digital Humanities.” Los Angeles Review of Books. Accessed September 15, 2018. https://lareviewofbooks.org/article/literature-is-not-data-against-digital-humanities/.

.	Ramsay, Stephen. “The Hermeneutics of Screwing Around; or What You Do with a Million Books,” n.d., 9.

.	Samuels, Lisa, and Jerome J. McGann. “Deformance and Interpretation.” New Literary History, vol. 30, no. 1, Feb. 1999, pp. 25–56. Project MUSE, doi:10.1353/nlh.1999.0010.

.	Syme, Holger S., and Scott Selisker. “In Defense of Data: Responses to Stephen Marche’s Literature Is Not Data.’” Los Angeles Review of Books. Accessed September 13, 2018. https://lareviewofbooks.org/article/in-defense-of-data-responses-to-stephen-marches-literature-is-not-data/.

.	“What Happens When an Algorithm Helps Write Science Fiction | WIRED.” Accessed September 13, 2018. https://www.wired.com/2017/12/when-an-algorithm-helps-write-science-fiction/.

A Critique of Kirschenbaum’s Essay Based On What Liu Already Said

Kirschenbaum’s essay, “What is ‘Digital Humanities,’ and Why Are They Saying Such Terrible Things About It?” (2014), speaks as if the Digital Humanities were different, as a construct, as if it were different from any other developing discipline. But, even old disciplines if seen from far enough away allow one to imagine them as constructs or dwellings surrounded by space. His essay attempts to define the Digital Humanities at a particular state during its development (2014) by analyzing the way the term “Digital Humanities” surfaces in speech and writing. To me, this type of critique is vacuous in that it may be either reduced or expanded to the zero point of meaning over time. Except in reference to Liu’s essay, “The Meaning of the Digital Humanities” (2013). He talks about the conception people have rather than explicitly stating that Digital Humanities is a defined discipline. In other words, the author implies that the way that people talk about a discipline can in some way alter the results and methods that that discipline produces. I can say, as a member of the What Every 1 Says project and a former member of many projects at Jet Propulsion Laboratories, that Digital Humanities functions as a discipline.

Digital Humanities produces results, none less than an astrophysics or mechanical engineering department. It is the lack of what the Digital Humanities produces in Kirschenbaum’s essay that I found conspicuous and a detraction to his consistent use of a “construct” (without a center, postmodern) analogy. Digital Humanities produces results such as finding that the frequency of the word “the” may be used to question why “the” appears more in Gothic novels than in other genres. Another result is that books of a specific genre may be found within a compendium of books. And according to Liu, the Digital Humanities utilizes a method of “multimodal, dynamic, and participatory design ”to arrive “pattern understanding;” Science and Technology “is [a] method for knowing meaning in the digital humanities” (Liu 416). Use value and knowledge, of the methods and results, produced by Digital Humanities are what disciplines create.

Liu focuses his “essay on digital literary studies” and how the digital humanities arrives at meaning valued by humanists (Liu 420). The meaning of what the Digital Humanities is is what defines the discipline, and this is what Liu describes in his essay. By explaining how the quantitative results, the numbers crunched by the computer, arrive at semantic meaning which originated in the HTOED, are carried through to the analysis, he shows the reader that themes generated by a computer software program aid in the critique of many volumes of literature. According to Liu,

> Lines of interpretation generated by machine observation--”supported the author's [of the software called the correlator] thesis that the “‘values of conduct and social norms’” in “‘knowable communities’” declined in the face of “‘urbanization, industrialization, and new stages of capitalism’” and “the discovery of precise word cohorts [semantically established through the use of the HTOED] giving genuinely fresh insight into the thesis, enables [the authors] . . . to offer more recognizable normative literary and cultural criticism, touching on action, setting, and character. (413-14)

In this way then Liu reveals that Digital Humanities is a disciplinary identity of the Humanities--Digital Humanities has as its basis the humanities.

Since Liu had already defined the Digital Humanities as a discipline, and since Kirschenbaum references Liu’s essay I found Kirschenbaum a bit disingenuous in his definition of the Digital Humanities as a kind of evolving consensus of discourse about the Digital Humanities. Kirschenbaum even admits so far as to say “of course one should ask questions about any set of disciplinary practices as visible and prodigious as digital humanities” and further: “‘digital humanities’” is, in fact, a diversified set of practices, one whose details and methodologies responsible critique has a responsibility to understand and engage” (Kirschenbaum 14). So, the author of “What is ‘Digital Humanities,’ and Why Are They Saying Such Terrible Things About It?” uses a postmodern construct focused at the edges--discourse about what the digital humanities is-- when it is an already defined (by Liu in 2013, one year before Kirschenbaum’s essay) and growing discipline. The same type of critique concerning language used by people speaking about the intersection of disciplines may be made at any time, but it is especially disheartening to see an author throw mud on a previously defined discipline as it struggles to become more broadly known.

I recognize the value of Kirschenbaum’s critique because it describes ways that people may talk about what Digital Humanities is and how discourse defines a discipline. But, because he speaks as if the dialogue about the Digital Humanities defines the Digital Humanities he misses the point of Liu’s essay which is that the digital humanities is a disciplinary identity of the humanities. Kirschenbaum, in my opinion, would have better stated his title as “At the periphery of the Digital Humanities: How people speak about unknown disciplines.”

                              Works Cited

Kirschenbaum, M. “What Is ‘Digital Humanities,’ and Why Are They Saying Such Terrible Things about It?” Differences 25, no. 1 (January 1, 2014): 46–63. https://doi.org/10.1215/10407391-2419997.

Liu, A. Y. “The Meaning of the Digital Humanities” 128, no. 2 (March 1, 2013): 409–23.                              https://doi.org/10.1632/pmla.2013.128.2.409.