Andrew Ravenscroft (C.Psychol, AFBPsS, PhD, FRSA) is a Psychologist and Learning Technologist who is a Professor of Education in the Cass School of Education and Communities at the University of East London (UEL), where he is Director of the International Centre for Public Pedagogy (ICPuP). He has a leading international profile in Technology Enhanced Learning (TEL) and socially responsive interdisciplinary research, with over 160 publications and being a principal or co-investigator on a broad portfolio of projects funded by various national and international agencies that have attracted over £6.4 Million. His expertise includes learning dialogue, critical thinking, design-based research, AI, big data, non-formal learning, complex educational interventions and interdisciplinary research
Colin Allen (PhD) is Distinguished Professor of History & Philosophy of Science at the University of Pittsburgh Changjiang Chair Professor at Xi’an Jiaotong University. He was previously Provost Professor of History & Philosophy of Science & Medicine and of Cognitive Science at Indiana University, where the research described in this article was initially carried out. His research spans animal cognition, philosophy of cognitive science, text mining of historical texts, and philosophical and ethical issues arising in AI. He is also co–author of a logic textbook, and associate editor as well as principal programmer for the work flow system used to manage the Stanford Encyclopedia of Philosophy.
This is the source
Skillful identification and interpretation of arguments is a cornerstone of
learning, scholarly activity and thoughtful civic engagement. These are
difficult skills for people to learn, and they are beyond the reach of current
computational methods from artificial intelligence and machine learning, despite
hype suggesting the contrary. In previous work, we have attempted to build
systems that scaffold these skills in people. In this paper we reflect on the
difficulties posed by this work, and we argue that it is a serious challenge
which ought to be taken up within the digital humanities and related efforts to
computationally support scholarly practice. Network analysis, bibliometrics, and
stylometrics, essentially leave out the fundamental humanistic skill of
charitable argument interpretation because they touch very little on the
Dicusses a technological platform and problems in scaffolding a system for argument identification and interpretation
We speak, for example, of an
This sentence appears in the first edition of
Margaret Floy Washburn’s textbook angry
wasp.The Animal Mind: An
Introduction to Comparative Psychology, published in 1908. It occurs as
part of an argument she presents against the anthropomorphic idea that we humans
can use our introspection of anger to understand the emotions of organisms so
physiologically and anatomically different from us. One suspects that Washburn,
whose story deserves more space than we can give it here, was intimately
familiar with anger. She was the first woman to earn a PhD in psychology in the
United States — albeit not from Columbia University, where she wanted to study.
Columbia were unwilling to set the precedent of admitting a woman for doctoral
studies. Instead she received her degree from Cornell University, where she was
accepted to the Sage School of Philosophy under the mentorship of Edward B.
Titchener, the pioneering psychologist who pursued a combined introspective and
experimental approach to the human mind. Washburn’s textbook would go through
four editions, spaced roughly a decade apart, spanning one of the most
consequential periods for psychology in its protracted separation from
philosophy as a new experimental discipline. After World War II, Washburn’s book
faded from view. We discovered it in the digital haystack of the Hathi Trust
with the assistance of computational methods we deployed to help us locate
argumentative needles such as the sentence leading this paragraph, the kind of
process one of the present authors describes elsewhere as guided serendipity
Our goal in this essay is to urge more attention in the digital and computational
humanities to the important scholarly practice of interpreting arguments. We
describe what we learned from our attempt to take an argument–centered approach
to humanistic enquiry in a big digital repository. We acknowledge that the
methods and approach we adopted represents an initial attempt to explore a
complex digital humanities problem, and can be improved upon, as one of our main
aims is to draw attention to this problem and spur further work in this area. We
believe we have provided a road map to guide future work — or, at least, an
analogue to one of those early maps of the world drawn by explorers, no doubt
distorting the major land masses, but better than nothing. If not dragons, wasps
lie here, and although much of the work described here involved good
old-fashioned human interpretation, our discovery of Washburn’s textbook and the
angry
wasps therein can be credited to the power of the computational
methods we used to locate arguments about the anthropomorphic attribution of
mental qualities such as anger to nonhuman animals.
Some of our work has been previously outlined in other publications that focused
on our multi-level computational approach angry
wasps and another about the cognitive powers of
spiders.
Automated argument extraction, also known as argument mining, has significant
challenges and remains a holy grail of artificial intelligence research (e.g.,
see Mochales and Moens, 2011; ACL, 2018). Our approach contributes only minimally
to solving that problem
We focused on the early 20th Century debate about animal minds because, in the aftermath of Darwin’s revolutionary effect on biology, it was a particularly fertile arena for historically important arguments that were still poised between scientific and literary styles of writing, and also for the pragmatic reason that it fitted our prior expertise in psychology, ethology, and philosophy of cognitive science. The debate remains lively in academic circles more than a century since Washburn published her book, and it is, of course, important to the ongoing public debates about animal welfare and animal rights. A close reading of Washburn’s text reveals to a modern reader a mixture of familiar and unfamiliar arguments, many of which deserve revisiting today. Our work also led us to five other texts (described below), which present a similar mixture of the familiar and the unfamiliar. Anyone who engages closely with the arguments in these books learns much about the trajectory that psychology in the English-speaking world was on, and also comes to understand how current debates about animal minds are dependent on the paths laid down these earlier authors.
The late 19th century and early 20th century was a period of significant
development for psychology that was characterised by important and competing
arguments. Experimental methods were on the rise, and psychologists, who had
often been housed in the same university department as the philosophers, were
professionalising, forming their own associations and journals, and their own
departments. Philosophy could be seen as retreating from the arguments based on
experimental evidence increasingly favored by psychologists, while psychologists
were wondering which of their received concepts and theories should be
jettisoned, and which could form the basis of further empirical investigation.
Such questions were particularly acute in animal comparative psychology. On the
one hand, Darwin’s theory of evolution exerted a strong pull towards the idea of
mental continuity between humans and animals. On the other hand, many Darwinians
were seemingly content with anecdotal evidence of animal intelligence to make
their case on analogical grounds to human behaviour, leading experimentally
inclined psychologists to reject such anecdotes and analogies as
anthropomorphic
. Even as the disciplines of psychology and philosophy
were formally disassociating themselves, philosophical arguments about the
proper
way to study animal psychology were becoming even more
prominent among the psychologists themselves.
While comparative psychology in the immediate post-Darwin era was a particularly
fertile era for the interplay between philosophy and science, the domain we
selected is not special. It serves as a proxy for any domain where
interpretation remains open and debate inevitably ensues. The lessons learned
from our attempt to find and interpret text about anthropomorphism in
comparative psychology generalise to other domains. There is no substitute for
reading the relevant texts closely, but there is similarly no substitute for
computational
The skills involved in interpreting arguments are essential in supporting and developing critical thinking and writing skills – even, and especially, where digital media predominate (e.g., Wegerif, 2007; Ravenscroft and McAlister, 2008; Ravenscroft 2010; Pilkington 2016). The volume and variety of this digital sphere provides opportunities for thinking, learning and writing within and across educational, professional and civic contexts. Across these contexts the need to identify, understand, and critically compare arguments is particularly important today to counteract a discourse in which accusations of ‘fake news’ and appeals to emotion are used to promote simplistic, insufficiently contextualised arguments and propositions, often overriding well evidenced and supported positions on a subject. There is a pressing need to support and promote scholarly practices focused on identifying, understanding and comparing written arguments that can occur within texts in massive data or document repositories.
The availability of massive document collections transforms the scale and complexity of the tasks of searching for and interpreting arguments, but these collections hold out great potential for understanding the academic and broader cultural contexts in which these arguments were historically and are presently situated. A key inspiration for our approach was to help inexperienced scholars simulate the way an experienced or expert scholar moves from macro-level views of document collections to micro-level close reading and interpretation of the key arguments in particular texts.
Of course, there will always be ethical issues, linked to any sociological and political framing around decisions about which digital collections to focus on. For example, the extent to which these may or may not be not-for-profit and available to the public. In our case, we worked with the HathiTrust collection, because it is a consortium of mostly public state universities – spearheaded by Michigan, Illinois, and Indiana – who retain ownership of the scanned content, up to the limits of the applicable copyright laws, although Google supported work to accelerate the scanning of these materials. The original proof-of-concept tool-set that we are proposing and discussing in this article is aimed at gaining insights, both conceptual and technological, about finding and interpreting arguments in digital repositories of any kind in principle. Therefore this work is aiming to be relatively generic in its positioning around what repository to focus on, although for pragmatic reasons also, the HathiTrust was particularly suitable because project members, and one co-author (Allen), were working at Indiana University at the time of this project, which facilitated the cooperation with the HathiTrust Research Center.
Our approach was also inspired by prior work on the methodology of
below the level of the work, i.e., a level where
Neither the exact implementation of standards, nor their integration into local communities of practice, can ever be wholly anticipated
Consider the challenge facing learners and researchers confronted with massive, digitised document collections that are not readily browsable in the way that shelves of library books once were. For one thing, many of the books of interest have been physically shifted to deep storage facilities and must be called up one-by-one rather than whole shelves at a time. (In a recent article, Jennifer Edmond (2018) laments the loss of serendipity this entails.) For another, the digitised collection represented by the HathiTrust Digital Library is an order of magnitude larger than any single library collection, so what was one shelf may have become the digital equivalent of ten. When browsing shelves of physical books, readers might pull a book off the shelf, sample a few pages from the book, and decide whether to put it back or to check it out of the library for closer reading. In the digital library, that decision takes on a different character: on the one hand there is a sense in which we don’t have to put anything back as we can carry out macroscopic analyses of very large numbers of texts; on the other hand we must still make selections for the closer readings that provide valuable insights that are currently beyond the reach of algorithms.
It is our view that a tool that links searching of massive document collections
to close critical reading of key arguments therein would have significant value
across educational contexts. It could make the practices of experienced scholars
more systematic, efficient and powerful. Perhaps more importantly, it could
empower and support less experienced learners to engage in systematic critical
thinking and reasoning linked to identifying and understanding arguments, which
is a well-attested challenge throughout education (e.g., see Ravenscroft et al., 2007; Andrews, 2009; Ravenscroft 2010). Although previous research has shown the value of
argument mapping to support greater sense making
and learning in general, this work has involved standalone
mapping tools
At the time we conducted the work upon which we base our discussion here, public
access to the HathiTrust Digital Library was restricted to the approximately
300,000 volumes outside copyright and in the public domain in the United States.
The HathiTrust now provides non-consumptive access to over 17 million volumes
(as of November 2019), increasing the challenge of identifying key texts from
unreadable quantities of text for the purpose of close reading and argument
extraction, making it even more important to develop techniques and tools such
as those we discuss here. A primary challenge at this scale concerns how to
identify and compare argumentation and arguments within and across texts, in a
way that is analogous to the way a scholar works, moving from a macro-level view
of texts to the close critical reading of particular arguments within and across
texts. This work (whose technical details are reported by McAlister et al. 2014 and Murdock et al. 2017) represented the first time
that topic modeling and argument mapping had been combined in a process that
allowed a scholar to identify pages within texts that should be fed into the
argument mapping task, both necessitating and supporting a close critical
reading of those texts by the individual engaged in the process. This work,
through ostensibly technical research combining Big Data searching and AI
techniques, included a broader exploration of the possibilities for integrating
science mapping and visualization, along with an initial attempt at argument
extraction
Texts do not give up their meanings easily, and different branches of the humanities bring different interpretative strategies to bear on the very same texts. For instance, philosophy students and scholars seek to understand conceptual frameworks and arguments that are typically not fully explicit in the texts they study. History students and scholars studying the very same texts may seek different kinds of clues to assist in their interpretations, such as facts about the social and cultural milieu in which they were written, or the specific contacts and experiences that led to particular acts of authorship. Literature students and scholars may focus on narrative structure in those texts, and the extent to which a given piece of work follows or flaunts literary conventions.
When the goal is also to exploit large datasets in support of traditional
humanities research and learning, it is necessary to answer the question of how
computational methods might help these kinds of students and scholars alike. For
instance, consider the history scholar or student who already knows the
biographical details of a 19th Century author, but wants to understand the
narrative or argumentative structure of specific passages in that author's work.
Scientometric methods such as the analysis of co-author and citation networks
To serve scholars and their students well, it is necessary to develop techniques for deeper analysis of the texts they care about. Sophisticated quantitative analysis of the full contents of texts will be needed. But computational methods alone will not suffice. Progress towards more effective use of massive text repositories will require a combination of computational techniques, digital curation by experts, and a better understanding of the way texts are critically understood and used in scholarly practices. No single method alone holds the key. Researchers and students need to be able to engage with the texts and discuss them with peers. Students and interested amateurs can in turn benefit from the discussions among experts if those can be adequately summarised and represented. People participating in debates may benefit from being able quickly to locate sources, both ancient and modern, that support or controvert their positions. There are many open research questions here about the design of effective systems that can serve scholars, and facilitate the representation of their knowledge in ways that others, experts and non-experts alike, can make use of in their critical engagement with the texts.
It is somewhat self-evident that massive document repositories offer access to an unparalleled number of texts across historical and disciplinary dimensions, opening up new possibilities for learning and scholarly activity. But, in practice, with so much choice about what to read, how do we decide which texts and parts of texts to focus on? And similarly, how can we focus on the key arguments within these texts to support the close reading and understanding of them? This is not just valuable in itself, it also counters the practice of reading texts in a fast, superficial and uncritical way, which is the temptation when we have access to such a massive quantity of text and information.
Previous attempts at automated argument identification (e.g., Moens et al. 2007) have focused on key words and
phrases which may indicate the introduction of premises (for this reason
,
in virtue of
, etc.) or conclusions (hence
, therefore
,
etc.). However, given a) the enormous variety of such markers, b) the
historically shifting patterns of usage, and c) how many arguments are presented
without such markers, such approaches can have significant limitations. Even
when enhanced to use grammatical structure
The set of documents accessible via the HathiTrust provide a robust test of our
approach, as particular difficulties of understanding arguments from this
historical era are: a) not all the content is congruent with the style of
scientific thought and writing that we have come to expect in the modern era
(e.g., the heavier reliance on anecdotal evidence in earlier times); b) the
language used even in scientific publications is indirect, and verbose compared
with its modern-day equivalent (e.g., there may be long digressions), and c)
what passes for acceptable argument may well have been different in that era
(e.g., the variety of rhetorical strategies). This problematisation contrasts
significantly with other formal approaches to argument modeling, that have
focused on articles with a modern, formulaic structure, e.g., in legal contexts
modern
scientific
articles
free runningsocial science or philosophical (and historical) texts could be considered an
order of magnitudemore challenging than previous work into argument mapping (e.g., Lawrence et al., 2012; Kirschner et al., 2012).
Most scholars are interested in arguments not simply for arguments’ sake, but
because of the underlying topics and issues that are addressed in those
arguments. Computational methods offer a variety of ways for capturing semantic
relations in text. Some, such as Latent Semantic Analysis (LSA)
LDA topic modelling (LDA-TM) is by now a familiar technique in the digital
humanities. It uses machine learning to represent documents as mixtures of
“topics” and these are represented as probability distributions of the words in
the corpus on which the model is trained. The training process automatically
assigns probabilities to the topic-document and word-topic distributions in such
a way that a relatively small set of topics (set by the modeler via a
hyperparameter
Going beyond the previous overview of our work by
Murdock et al. (2017), here we focus in more detail on the pedagogical
practice, through the link between the original discovered
arguments
, and to problematise the design space.
Automated selection from large volume sets is necessary because one cannot hope
to inspect by eye the whole collection. For example, although a standard keyword
search in the HathiTrust collection, using Darwin
, comparative
psychology
, anthropomorphism
, and parsimony
, reduced over
300,000 public domain works to a list of 1,315 volumes, this many books is on
the order of Charles Darwin’s entire personal library, accumulated and read over
several decades. To help us to decide what to read?
we chose to adapt
topic modeling to our purposes. This technique is useful for information
retrieval because it allows a higher level of semantic abstraction than keyword
searching.
LDA topic modelling (LDA-TM) was first introduced by Blei
et al., (2003), and it has been subsequently deployed in a variety of
applications naïve
approach to this, simply using the
proportions of the documents assigned by the model to the topics of
interest, and then choosing a threshold on the proportions that seemed to
the person making the choice to be sufficient to capture the books relevant
to comparative psychology (along with many irrelevant ones; i.e., we
preferred recall over precision at this stage). See Murdock et al., (2017) for details.
The six volumes selected by the methods described above each discuss our chosen topic of Animal Psychology:
We decided to adopt the visual argument mapping approach for a number of
related reasons. Previous research has strongly supported the value of
argument mapping for: greater sense-making
of
argumentative texts lens
and
provided a standard representational scheme that could be applied across the
different texts, showing the found
arguments in each. Once mapped,
these representations can be potentially re-used and shared in further
argument inquiry or tool development. Further details of the mapping tool
and process, and how it was used to interpret the texts and arguments that
are specific to our study are provided below.
The rating of pages according to their loading on topics of interest was
taken as an indicator of material worthy or argument analysis and mapping,
but these were not used to limit arguments that started before or ended
after the rated pages. Thus, each argument selected by the person doing the
mapping spanned rated pages, but may also have spanned unrated bordering
pages occasionally. Also, not all rated pages that dealt with the chosen
topic contained argument. Table 1 (below) shows
the Pages that were selected from each Volume, following our topic modelling approach, and
also the number of Maps for each Volume. This shows that the first three of the listed volumes,
according to our topic modelling returns were potentially argument
rich
, with their arguments therein creating 15, 10 and 8 maps
respectively. For The Animal Mind, which contained
many more rated pages than listed in the table, we chose to limit our
analysis to 40 pages constituting the largest blocks of contiguous pages
containing pages with greater than 90% loading on the topics of
interest.
The latter three in the list were potentially less rich in argument, creating 2, 5 and 3 maps respectively. This difference indicates the variability in writing style during this historical period, with some texts showing clearer lines of argument than others.
factsto present. The fifth text is based on predominantly personal observation, so, it is a piece of anecdotal comparative psychology, and not concerned with the methodological questions that lead to the argumentative structure of Washburn’s book. The final text has fewer arguments because it is a
pop-sciencebook and is more engaged in telling a triumphal narrative of scientific progress, rather than dealing with controversies in the field. It does have a section on animals that emphasises the discoveries that seem to show how intelligent they are, so it does not aim for the sort of complex analysis that is provided by Washburn. So, considering these findings lends support to our assumption that the
topic richtexts according to our topic modeling method also approximate the degree to which the content is
argument rich.
The argument content was mapped using OVA+
To identify the form and structure of the arguments contained in the selected
texts we adapted a generic approach for manual argument analysis described by Lawrence et al. (2014). Through
considering this work we developed a bespoke rubric that standardised and
described the interpretative process that linked the analysis of our
historical
texts to the argument format of the mapping tool. This was
informed by the members of the team with expertise in the humanities, who were
familiar with the styles of writing about this topic for this historical period,
and the researcher who was performing the mapping process. This was important in
our case because, as mentioned earlier, the natural
arguments contained
in these texts, demanded more sophisticated interpretation compared with other
applications where the arguments were more clearly defined. The full detail of
this interpretative rubric can be accessed online https://bit.ly/35CshTD. To summarise
it for the purposes of this paper:
because,
therefore,
suggesting thatetc.) where these are present
Through interpreting and mapping the identified arguments in these texts the
researcher produced the 47 OVA+ maps covering the selections from the six
volumes, which can be viewed online
The argument map (Arg 3) above contains text taken from
RAboxes contained in the directional arrows demonstrates that the propositions on the left (P1, P2, P3) support the conclusions on the right (P5, P6), where the latter are also interconnected, as indicated through pointing to a shared relation (an
This approach was particularly appropriate for the volumes that we analysed, where, in some cases, the same topic is pursued for a complete chapter and so there are opportunities to map the extended argument. Given the way the arguments were differentially expressed, with some text being more easily mapped compared to others, the mapping process was quite sophisticated, yet followed the standardised rubric to maintain consistency of interpretation.
This deep
identification, representation and interpretation process
linked to the subsequent argument maps, including careful reading of the
identified texts provided a double lens
onto the arguments that
provided a stronger interpretative platform than if these methods had not
been applied. The identification, representation and mapping process was
performed by a researcher who was familiar with the basics of argument
mapping, who was neither a domain expert in comparative psychology nor
experienced with extracting arguments from this kind of textual
material.
[In this first edition of her textbook, destined for four editions]
Washburn sets the context for the debate on animal consciousness. She
meets the charge that animal psychology is necessarily anthropomorphic
straight away, and admits there is a problem (Arg1). She introduces
Montaigne’s arguments for animal intelligence based upon the similarity
of human and animal behaviours (Arg2) and follows with Descartes’s
opposing argument, that animals are clock-like machines, with no
capacity for thought (Arg3). Washburn next presents Darwin as arguing on
the basis of analogical claims, such as that animals reason because they
are seen to pause, deliberate and resolve
.
She asserts that Darwin's aim of defending his theory of evolution in
face of ongoing controversy about the mental and moral gulf between man
and animals, means that his claims cannot be taken at face value (Arg4).
In contrast many physiologists argue that psychic interpretations are
less preferable than biological explanations of animal behaviour in
terms such as tropism [unconscious reaction to stimulation] (Arg5).
Washburn next summarises three main anti-mentalist camps or positions in
the field (Arg6). She criticises the physiologists, the first camp, for
ignoring or simplifying phenomena to fit a predetermined theory, and she
argues that their approach yields a
Washburn argues for a cautious approach to animal psychology,
acknowledging pitfalls and problems but seeking scientific methods to
overcome them (Arg11). She introduces Lloyd Morgan’s [famous] Canon
whereby the simplest level of psychic faculty for an animal should be
assumed that can fully explain the facts of a case. She argues that the
choice may not always be the right one, but at least it reduces
anthropomorphism by compensating for a known bias (Arg12). Washburn next
argues against Loeb’s suggestion that learning by
experience
is a conclusive criterion for mind, but cautions
that absence of proof does not amount to disproof. She maintains that
rapid learning practically assures mind, but holds that great
uncertainty remains about consciousness in lower animals (Arg13 and
Arg14). Morphology and similarity of animals’ physiology to humans’ must
be taken into account in deciding if an animal is conscious or not, and
degrees of similarity indicate a gradation of consciousness, from lower
to higher animals, with no possibility of drawing a sharp line between
animals with and without consciousness (Arg15)
[Eric Wasmann was a Jesuit priest and naturalist, publicly renowned for his books about the variety of amazing ant behaviours.] Wasmann’s concept that “intelligence is a spiritual power” leads him to the claim that if animals had this spiritual power “they would necessarily be capable of language”. Animals don’t speak, so animals don’t have intelligence (Arg1). He supports his views of ants by reference to observations made by Aristotle, Stagirite, St Augustine, [and Wasmann’s contemporary naturalist] Dubois-Reymond (Arg3). Wasmann denigrates suggestions by ‘modern sociologists’ that ant “states” and human republics can be equated, explaining that class differences arise from ‘conditions of life’ or ‘intelligent’ free choice in Man, but ant castes arise from organic laws of polymorphism [multiple body forms] (Arg4). Wasmann asserts animal intelligence is really sensile cognition and sensuous experience, but if higher animals are credited with intelligence, it would be inconsistent to deny ants the same (Arg5). He argues that ants achieve a more perfect level of social cooperation than even the higher vertebrates, such as apes (Arg7).
Wasmann criticises Darwin for his anthropomorphic stance towards the
‘silence and obedience’ of a group of baboons, which Wasmann
reinterprets as ‘fidelity and obedience’, and takes to imply
‘reasonable, voluntary subjection to the demands of duty and authority’.
He argues that the more likely explanation is “the instinctive
association of certain sensile perceptions with certain sensile
impulses” (Arg6). This association removes the need to allow animals
thought; instead, instinct is a sufficient explanation (Arg10). The
author explains that instinct has two elements, ‘automatism’ of
behaviour (generally found in lower orders of animals) and ‘plasticity’
of behaviour (generally found in higher orders). Because the
architecture of ants’ nests varies from species to species even when the
physical attributes of the ants are highly similar, he argues that a
simple explanation of the variety of architecture linked to physical
attributes will not do; rather the decisive factor is the psychic
disposition of the ant species (Arg8). Wasmann maintains that while ants
‘verge on heroic unselfishness’ towards their young, only ‘Man’ is
conscious of duty and the morals of parental love. Although he admits
that some aspects of motherly love in humans are instinctual, motherly
love cannot be attributed to animals because it is ‘spiritual’, based on
awareness of duty that is unique to humans (Arg9).
The section above demonstrates a sophisticated close reading of a sample of the arguments in the two selected texts, through incorporating the mapping approach into the interpretation process. For example, the comparison and contrast afforded by Washburn’s survey of the arguments in the literature and her attempt to articulate a good scientific methodology for comparative psychology. This contrasts with Wasmann’s more polemical and theological approach to the perfection of behaviour through instinct, which reveals that despite Darwin’s work, published nearly 50 years earlier, much of the controversy revolves around whether humans have a special, perhaps God-given position, separate from the animal world.
A number of historically important themes emerged from the interpretation of the arguments in the six volumes that are given in full in McAlister et al., (2014). These demonstrated the ability of our selection and argument mapping methods to allow a reader, who was previously unfamiliar with the scholarship in this area, to zero in on the relevant passages and then acquire an understanding of the key themes, which is a measure of the success of those methods. Although it was not a primary goal of our project to produce new insights into the domain-specific content, these would somewhat hopefully and inevitably emerge from the close critical reading of the key arguments. So it is worth making some concise, content-specific remarks here about two of the themes that emerged from the six volumes, to demonstrate the potential value of the proposed approach.
(i) Animal Flexibility. All the authors,
evolutionists and non-evolutionists alike, were willing to recognise
hitherto unacknowledged flexibility and variability in behaviour of
individual animals. They all identify the same extremes – excessive
anthropomorphism on the one hand, and the conception of animals as
automatic reflex machines on the other – but each claims the middle
ground for their quite different positions! Even Wasmann, the lone
anti-evolutionist in our sample, denies that individual ants are reflex
machines, claiming that the flexibility of individual ants is of a
psychic variety
not mechanical automatism
, although he attributes
this flexibility to instinct
not
reason.
(ii) Developmental Approaches. Three of the
authors, Mills (1888), Reid (1906), and Needham (1910), explicitly advocate a
developmental approach to the study of animal mind, operating within the
framework of a strong nature-environment distinction (corresponding to
today’s nature-nurture
distinction). They
make the case for comparative developmental studies, particularly
experimentally rearing animals in isolation.
Although the accounts (above) of the interpretation of the arguments are
relatively concise, they demonstrate a successful
The approach described in this article offers an initial prototype of a design
for scholarly interaction with technology that begins with topic model-assisted
search of massive document repositories and leads to close critical reading of
the arguments in the texts therein. It has also produced important insights
about the way these arguments are rendered
and interpreted by a person
new to such historical texts and work in the humanities. The automated content
selection and categorization work described in this article demonstrated the
feasibility and reliability of large-scale, fined-grained topic-based
categorization across a range of topics in science and philosophy using
documents defined at a variety of scales (whole books, book pages, and
individual sentences in books). Categorization and selection are essential
first-steps in the scholarly process of identifying further structures, such as
arguments, in large data sets. Although it might have been possible to construct
more sophisticated keyword searches using Boolean operators to identify the same
pages of interest for our analysis, this would have required painstaking trial
and error, whereas the topic modeling provided a relatively straightforward
semi-automatic approach to narrowing down. A number of insights emerged from
performing the human interpretation of texts that were delivered by our topic
modeling techniques and then mapped in argumentative terms through the
argument-mapping tool OVA+.
Topic modeling was clearly successful in identifying the texts (chapters and
pages) that contained the ‘stuff’ of arguments linked to the keywords and topics
that were searched for, strongly supporting our assumption that we could
approximate
Furthermore, we were able to leverage the human-constructed argument maps against
a micro-level topic model trained on a single book with each sentence treated as
a document
. Such an approach to Washburn’s
angrywasp. Close reading was essential to determine why certain sentences were selected by this method. For example, the relevance to anthropomorphism of the sentence,
This, of course, does not refer to the power to judge distance,was not immediately evident. The context of this sentence in Washburn’s footnote on p.238 is as follows:
Porter observed that the distance at which spiders of the generaArgiope andEpeira could apparently see objects was increased six or eight times if the spider was previously disturbed by shaking her web. This, of course, does not refer to the power tojudge distance.
To summarise, here are five key points from this study:
gap fillingby the mapper, but this was cognitively valuable in supporting argument identification, representation and understanding linked to close critical reading. Some types of argument, e.g., historical arguments, are not simply latent and waiting for identification and representation. Rather, the arguments
come alivethrough interpretation and the processes of mapping and then writing about them.
Our emphasis on investigating and testing the feasibility of our computational tools to support existing scholarly practices of identifying and understanding arguments in digitised texts has meant that thus far we have deliberately prioritised validating technical possibilities over systematic empirical testing with different texts and/or different scholars. This suggests the need for further research that would incorporate technical and empirical strands into the development of the human-computer interaction.
The technical implications are that the next tool-set, should more closely connect the topic modelling to the argument mapping. Robust tools for topic modeling already exist in the form of
Once a more integrated and user-friendly version of the toolkit is developed, it would support more systematic empirical investigation of the interaction between user and machine. Our hypotheses are that compared to unassisted argument identification and understanding, this approach would: find the argumentative parts of relevant texts much faster and with greater accuracy; scaffold deeper understanding; and, provide flexible and permanent representations that could be reflected upon, extended and re-used. Further and more generally, future work will accept the need to move towards an environment for constructing and developing representations of argument rather than simply mapping them.
The above appears a sensible conceptualization for future work, because through
implementing our methods it became apparent that arguments were rarely neatly
and clearly structured and defined explicitly in the texts. The historical
distance to these texts, and the shift in academic writing styles over the past
century served to make the task of Do arguments actually exist in clearly defined forms within
(certain) texts? Or, do arguments only take form when readers focus on
understanding them?
When today’s reader encounters the seemingly verbose
yet strangely enthymematic nature of yesterday’s arguments, what can we learn
about the interaction between readers and texts, and about the minds of the
authors and their original readers?
While these questions are too big to be answered by our original study, their
potential validity as important questions are, we argue, supported. The notion
that textual arguments are constructed through human interpretation is also
supported by the observation that argument structure is notoriously difficult
for people, even after training, to determine (see Oppenheimer & Zalta 2011, 2017 and Garbacz
2012 for an interesting example of disagreement among experts about
how to formalise Anselm’s famous
Design investigations such as the one we have described here must remain mindful
of the reconstructive nature of argument extraction.agreeing
or disagreeing
, or
liking
, or not, simple emotive propositions and arguments.
In the application of digital tools to the humanities, we must also be mindful that high-sounding rhetoric about civic engagement, the democratization of scholarship, etc., can be undermined by the facts surrounding the choice of sources and limitations of access to the materials analysed. In our case, because of the association between HathiTrust and Google Books, some may worry (incorrectly in our estimation) that, despite its origins and continuation in publicly-funded universities, the HathiTrust nevertheless represents the sort of corporatisation of higher education that some find undesirable. Whereas we accept that there will always be challenging issues concerning which repositories to focus on, from a scholarly practice perspective our position is clear. We want to improve and democratize the scholarly practice of finding and interpreting arguments, so that argumentative and critical meaning making is potentially more inclusive, in addition to supporting deeper inquiry for those who are already engaging in such practice.
The research described in this article tackled a complex problem of how to
investigate and design a technological platform that empowers and supports, or
fit the pieces together
. This work provides an important
problematisation of the design space for future tool development that should
arguably focus, not on automatically extracting arguments, but instead focus on
how to better interrogate, manipulate and understand them: a practice that has
increasing importance and relevance within and without the academy.
Edmond notes that the digital tools currently available to humanists, focused as
they are on text, do not fully reflect the much broader information gathering
practices of humanists, which, in her phrase, remain stubbornly multimodal
remote
storage and electronic catalogues diminish the likelihood for
serendipity
for reasons we already mention, we believe we have
outlined a digital research environment for argument-based analysis in which
serendipity arises. Following the traces provided by topic models led to
sampling a few books in more detail, and then to the wasps, spiders, and amoebae
that occupied the thoughts of comparative psychologists a century ago: creatures
that have all re-emerged in the 21st century in discussions of non-human forms
of cognition. The selections were assisted but not forced, allowing the
individual scholar to follow whatever leads looked promising in light of
whatever background information the scholar has gleaned from other sources.
Guided serendipity resulted, and thus the angry
wasp was found.
The research reported in this article derives from a project that was funded by the 2011 International Digging into Data Challenge. The project, entitled