[Humanist] 31.143 pubs: big data cfp
Humanist Discussion Group
willard.mccarty at mccarty.org.uk
Wed Jun 28 08:33:35 CEST 2017
Humanist Discussion Group, Vol. 31, No. 143.
Department of Digital Humanities, King's College London
Submit to: humanist at lists.digitalhumanities.org
Date: Tue, 27 Jun 2017 16:57:54 -0400
From: Amanda Licastro <amanda.licastro at gmail.com>
Subject: CFP: Composition as Big Data (due 8/1)
Dear Humanities Researchers,
Please consider submitting a proposal for the following edited collection.
Feel free to share widely (with apologies for cross-posting).
This edited collection, currently under consideration, will serve as a
research and methods guide for practitioners interested in conducting
large-scale data-driven examinations of writing.
Full CFP here: http://bit.ly/comp-as-data-cfp (which leads to
Computational analysis of big data has changed the way information is
processed. Corporations analyze patterns in what people buy, how far they
run, where they spend their time; they quantify habits to create more
effective advertisements and cross-promotions. In academe, humanities
scholars are using computational analysis to identify patterns in literary
texts, historical documents, image archives, and sound, all of which has
added to the body of knowledge in humanities theory and methodology.
Meanwhile, many institutions and writing programs are adopting learning
management systems that may digitally archive hundreds – if not thousands
or tens of thousands – of student compositions from across levels and
disciplines. What is our responsibility, and what is the potential, in
harnessing big-data methods as composition researchers, teachers, and
Composition and rhetoric scholars have begun to adopt corpus-based
computational analysis both to better understand the field as a whole –
through the rhetoric of job postings (Lauer), professional journals
(Mueller; Almjeld et al), and dissertation records (Miller; Gatta) – and to
research student compositions, the teaching of which is the primary job of
most composition and rhetoric scholars. Through data-driven studies of
student entrance exams (Aull), citation practices (Jamieson and Moore
Howard), revision practices (Moxley), and acknowledgment of
counterarguments (Lancaster), scholars have found patterns that distinguish
student writing from published academic writing, suggesting areas to target
This edited collection will model and reflect on the research made possible
by high-capacity data storage and computation, either alone or in
conjunction with close reading and evaluation in context. Authors are
invited to submit abstracts for chapters that focus on the rhetoric,
methods, and findings of recent large-scale data studies of writing. We are
especially interested in contributions that include replicable practices
and/or detailed descriptions of method, with an eye toward graduate-level
research, teaching, or administrative applications in the intersecting
fields of digital humanities, linguistics, and composition.
The following list of topics and questions is not exhaustive, but
suggestive, illustrating the range of issues to be taken up:
Data Capture and the Captivation of Data
When we say “big data” in composition what do we mean? What datasets
are available, promising, or already producing insight?
What new questions do these datasets allow us to ask or answer? What
are their limitations?
How has data gathered from large corpora of (student) writing changed
the scholarship and practice of composition / rhetoric? How
might such data
do so in the future?
Who is responsible for creating or curating datasets in composition?
How might the answers change at different scales?
What are the ethical responsibilities of anyone storing, retrieving,
or analyzing composition data – perhaps especially where
students and their
writing are concerned?
How, should researchers negotiate issues of consent and
representation when recording or reporting on data? How is this
the scale or scope of the data?
Discourse and Discovery
How can computational tools aid in the qualitative coding of
(student) writing? How do these practices relate to traditional coding
What data-supported models of writing practices emerge from the study
of digital corpora?
What does or can big data show about the nature of expertise and
learning in the context of composing?
How can the field of composition / rhetoric use data to positively
impact pedagogical or andragogical practices? For example, how can
data-supported studies improve composition instruction in higher
What is the relationship between distant and close reading in regard
to assessing student writing? Can and/or should distant reading practices
be applied to assessment at the undergraduate level, and in what ways?
What role can analysis of big data play for student researchers in
composition / rhetoric?
Supporting a Data-Supported Future
What standards or best practices are emerging for data archiving,
aggregation, and interoperability?
How might those new to big-data approaches most usefully manage
issues of scope or documentation?
How can we best support new researchers, teachers, or administrators
in developing comfort with big-data approaches and insights? What does a
successful program of big-data training look like?
Abstracts of approximately 350 words should provide, in as much detail as
possible, the focus and argument(s) for the proposed chapter. Abstracts and
brief author bios are due 1 August 2017 via Google Forms at
http://bit.ly/comp-as-data. Questions can be directed to Amanda Licastro (
amanda.licastro at gmail.com) or Ben Miller (benmiller314 at gmail.com) with the
subject line “Composition as Big Data.”
Amanda Licastro, PhD
Assistant Professor of Digital Rhetoric,
Stevenson University in Maryland
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