Faculty member in the Engish department at Brown University.
This is the source
This review finds that Jockers'
A review of Matthew Jockers'
A friend recently asked me, with a smile and in the friendliest
way possible, What does a professor of literature actually
do? Don’t you just spout off about the real meaning of a
great work of literature? Are your books really scholarship
or just your own opinions?
He sought me out after his
oldest daughter announced that she wanted to get a Ph.D. in
English. He had not only heard horror stories about the job
market for Humanities PhDs but was also absolutely baffled, even
after she explained to him, as to what literature professors
published and whether it had any value.
When I tell this story to my colleagues, they often refer to the
so-called crisis in the Humanities
as a way to
contextualize these questions as part of an attack on the
humanities in general. I believe this is, at least to some
extent, absolutely correct. Just focusing on English departments
(the subsection of the humanities I know best), universities
across the country report steep declines in the number of
students majoring in English; students who complete a PhD in
English face an abysmal job market in which full-time positions
are a rare species available to a decreasing number of
graduates; and those who get a job at a place where a book is
required for tenure face an increasing number of academic
presses reluctant to publish their first book because of the
plummeting sales of literary studies. My colleagues hear not
only the fears of a concerned father but also, and even more
powerfully, echoes of what administrators at their universities
ask them when they request new faculty positions (if, that is,
they are lucky enough to work at a university that is even
willing to consider expanding full-time positions in literature
departments).
The readers of this journal see that the digital humanities,
which has a long history dating back at least to the 1940s, has
emerged in this crisis as a major topic of conversation among
scholars and administrators associated with the humanities. I
will forgo offering here my own speculations about why DH has
gained the prominence it has amidst the ruins of the
institutional humanities. DH occupies, for journalists reporting
on the humanities and conference attendees in the humanities,
the very similar position of the next big thing
previously occupied by theory (which, like the term digital
humanities,
I mean not a unified or uniform set of
practices, ideas, projects, writings, products, ideologies,
methods, etc. but rather as a very imprecise shorthand for a set
of practices, etc. that are, for better or worse, grouped
together). DH appeals to me for precisely the same reason as
theory and, quite frankly, as the questions posed by the
friendly parent. DH and theory each ask those interested in the
humanities to ask themselves why we do what we do the way we do
it. What do we learn when we close-read a literary text? What
assumptions about our goals, aims, and values are embedded
within the methods of close-reading? What relationship does
history bear to literature — what, after all, is the difference
between the categories of history and literature, if any, and
what ends do these distinctions serve? Indeed, why do we have
literature departments at all?
Matthew Jockers’
a new methodologythat demands a
new way of thinking about [literary scholars’] object of study
a moment of revolution [in] the way we study the literary record
how— we study literature is changing in fundamental ways.
the study of literature should be approached not simply as an examination of seminal works but as an examination of an aggregated ecosystem oreconomyof texts
new knowledgeabout larger trends in literary history and, in the process, give analysts
a fuller sense of the literary-historical milieu in which a given book exists
data setsnow available to scholars make it possible to understand individual texts in relation to an almost
comprehensivecontext of other relevant texts
a blended approachthat puts data mining in conversation with close-reading of individual texts, an approach that, he contends, will allow analysts to
better understand the context in which individual texts exist and thereby better understand those individual texts
To make the case for the value of data mining in literary study,
Revolution,
Evidence,
Tradition,and
Macroanalysis— of the potential value of data mining for literary scholars.
Revolutiondifferentiates data mining from close-reading, the method he argues has come to dominate literary studies;
Evidencecontrasts the kind of evidentiary material data mining produces to the kind derived from a close, careful reading of a text by an individual analyst;
Analysisexamines data mining in relation to the history of humanities computing as a way of differentiating it from other forms of computational and/or digital approaches; and
Prospectsbriefly discusses some of the issues and questions the methodology of macroanalysis seems particularly well-suited to address, especially in contrast to close-reading. In Part II of the book, the Analysis section, Jockers uses data mining tools on a database composed of nineteenth-century Irish-American fiction to explore questions of Irish-American identity written by writers with Irish roots. If the first section can be said to lay out the theoretical justification for a macroanalytical method, the Analysis section aims to show that, in practice, macroanalysis can, first, yield knowledge about familiar topics that challenge a particular field of literary study’s conventional wisdom, a wisdom borne out of earlier models of analysis, and/or, second, generate insights about issues that hadn’t occurred to scholars in the field using conventional methods of analysis. Since Jockers focuses here on nineteenth-century American fiction by Irish-American authors about Irish identity, a set of authors and issues about which I have little expertise, I will refrain from judging the success of Jockers’ use of data mining in this instance. I do find it significant, though, for reasons I will mention below, that Jockers uses his computational tools to explore four analytical categories, categories which also serve as chapter titles for this section:
Style,
Nationality,
Theme,and
Influence.
From my perspective, as a scholar trained in the 1980s, when faculty in the vast majority of literature departments in the United States were entirely ignorant of humanities computing, I find myself excited and energized by the possibilities data mining offers.
What about data mining prompts my excitement? First, I think that we don’t know what we don’t know. While it might end up being true that data mining will provide us exclusively and absolutely only with insights and information that we already knew, I find this possibility to be exceptionally unlikely. Second, as Jockers points out so well in various places in
As I noted above, Jockers speaks of the turn to data mining as a
revolution. I think the word revolution
is well chosen.
Trends, relationships, patterns, and meanings in the histories
of literatures across the world that had previously been
invisible will come into view when we unleash the many digital
processing tools on the bits of data that are now readily
available. I think it is safe to say that these tools will,
taken as a whole, help us see anew the various literary
histories we study. Revolutions can occur, at least in some
instances, when one sees a familiar object or set of objects in
a fundamentally new way. So, it seems to me, if macroanalysis or
even other methods that grow out of data mining do, as I think
they will, produce new insights about familiar literary
histories and new ways of understanding literature — what it is,
what it does, how it should be understood, what it’s value is,
etc. — it will bring about revolutions in the way we
conceptualize, read, study, and teach literary works. Perhaps
even more exciting, by integrating explicitly computational
methods into literary analysis and hiring people who have
extensive computational skills in literature departments, we
will, I think, change the nature of literary study by broadening
the way it can be processed and understood. Works that advocate
the use of data mining and other computational methods such as
What Jockers and I find exciting and potentially revolutionary about data mining often do not coincide, though. I found that Jockers’ decision to focus on Style, Nationality, Theme, and Influence in his database drained the revolutionary potential out of data mining. From my perspective as a scholar of American literature trained in the 1980s, the categories Jockers choose seemed more counter-revolutionary than revolutionary. Each of these categories is quite old and familiar in the study of American literature. They constitute, in fact, foundational categories. The professional study of American literature in the academy can be dated to the 1920s and 30s. The flagship journal in the field,
On the one hand, the categories Jockers chose are foundational for a reason. They are, well, foundational. Sticking again with influence, I found it disappointing that Jockers chose not to engage with some of the most recent works that offer rather complex analyses, theories, meditations, etc. on the very notion of influence and what its various forms might look like. He cites Harold Bloom’s
influence— and style, theme, and nationality — has been discussed by writers subsequent to Bloom, writers who were/are part of the so-called theory revolution in literary studies, would have added greatly to Macroanalysis’ contribution to the current dialogue about the digital humanities. This revolution happened immediately after Bloom’s book and, to some extent, managed to remain the dominant way to read literature in the top journals in the discipline. At Brown, and many other peer universities across the United States, the theory revolution so transformed the way literature was studied and taught in English departments that few of my colleagues mention theme, style, or influence in their published writings (and, if they do, their use of these terms would be almost unrecognizable to the authors of the essays from
I have no idea whether Jockers wants his focus on such rather old-school categories understood in ways that predate the theory revolution to be part of a counter-revolution that will take back literary studies from the theorists. I do not find enough evidence in
objectivemethod of data mining in order to displace the admittedly less
objectiveanalyses one finds in works steeped in literary theory — and, quite frankly, it means little to me whether he intends the book to be part of a move to wrest literary studies away from the theorists. Influence, theme, nationality, and style remain important categories in literary study, in spite of the changes wrought in the field over the last fifty years, and they deserve to be studied in a variety of different ways. My point here is simply that, from my perspective, the provocative nature of data mining lies precisely in its potential to help us think about literature in
traditionaland
nontraditionalways. Jockers uses macroanalysis to learn about individual authors in relation to the economy of nineteenth-century American literature. I want to use data mining to learn about the economy of nineteenth-century American literature without recourse to individual authors. Jockers focuses on the potential of data mining to reveal things about individual authors and large movements in literary history. I am interested in how data mining might reveal trends that exceed the control of individual authors. In many respects, Jockers and I are not so far apart. He speculates that after much research we might find that literary trends obey an
evolutionarymodel — a speculation, whatever its plausibility, and I admit I am skeptical — that puts the question of individual human agency at the forefront
In spite of these differences in emphases, I found
Bring on the revolution.