











Change of Paradigm: From Individual to Community-Based Scholarship

Massimo Riva



Abstract: The change of paradigm we are witnessing involves not only the transformation of the procedures of scholarly research and learning but
also and more importantly the transformation of its goals. The scholarly
work is being increasingly made over into a collective, collaborative,
large-scale enterprise characterized by the move from discursive to graphic-algorithmic
forms of data presentation and interpretation. This change has eventful
consequences for critical thinking: as humanists engaged with a potentially
radical transformation of the community we belong to, our most daunting task is
how to transpose traditional scholarly practices onto the new platform,
envisioning new goals and outputs for our traditional tasks.


Let me clarify, first of all that my title does not refer to the application of knowledge through faculty engagement in
community-based research, teaching and service -- something that is usually
understood as "community-engaged scholarship." The change of paradigm
I refer to, instead, is of a cognitive kind and should be understood within a
broader framework: namely, the general transformation of our participatory or
convergence culture in the age of social and "spreadable" media (to use a
terminology made current by Richard Jenkins). Of course, as the Web 2.0 evolves
toward the Web 3.0 or the semantic web, community-engaged scholarship will be
one of the most crucial components of this larger and more pervasive phenomenon.


What is at stake is not only the transformation of
the procedures of scholarly research but also and more importantly the
transformation of its goals: traditionally based (at least in the humanities) on
the individual researcher and author, knowledge work, or the scholarly mode of
production, is increasingly transformed into a collective, collaborative
enterprise. However, this collectivization or socialization of research is
based on different models of what we mean by "collaboration." And this is what
I'd like to address here, very briefly. Digital technologies do promote the
implementation of collaborative, distributed or networked research practices
and outputs: however, these dramatically differ from each other in
methodologies and goals. To give you only one example: data mining in the
humanities can be defined as a collective enterprise not only because it relies
upon the input of cohorts, or even generations, of scholars, but also because
it shifts the traditional goals of humanistic research toward a "collective"
output, embodied in data aggregations or disaggregations
which often defy more traditional, individualistic methods of analysis. The
same could be said (and I would have developed this aspect in my presentation
in the other panel) of geo-parsing methodologies which connect (or superimpose)
geographical and historical, cultural or textual data onto each other, as
exemplified by geo-archaeological studies or even literary studies which make
use of geographic information retrieval techniques (mapping literary corpora such
as, for example, the history of the novel, or other literary genres, according
to their geographical distribution in time, for example).


Of course, individual scholars can still give their
individual contribution to corpus linguistics or contribute to the geoparsing and/or timelining of data by running if not composing an algorithm; and they can base their individual
analysis on the collective-automatized input. In either case, however,
individual contribution is neither the determining factor nor the most representative
output. It is not only the focus but also the ethos of scholarship to shift
with the widespread application of these new techniques. The general move from
discursive to graphic-algorithmic forms of data visualization and
interpretation (including textual data), for example, has eventful consequences
for critical thinking. In a sense, we are witnessing today a return to the age
of positivism. In short, what is changing is the way we make sense (in
the literal sense of the words). Raw data can only make sense to us if we
make them...


I will give you only one example, which has to do
with the scale of things: in the age of big data, the humanities, too, feel
compelled to increase the scale of their objects of study. Both Massimo Lollini
and I have focused our projects on individual works (Boccaccio's Decameron
and Petrarca's Canzoniere)
as well as their legacy or afterlife in translation, etc. Of course, these are
strategic works, so to speak, as they occupy a crucial position in the history
of literature (not limited to the Italian) and the history of their respective
genres. Yet, our approach is becoming increasingly counterintuitive as the
tools at our disposal become ever more powerful. Powerful computation applied
to small scale phenomena seems, and perhaps is, a waste. Mathematical or
algorithmic patterns applied to a singularity are absurd.


This change of scale
is even more daunting when we move from textual phenomena to visualization in
general: according to the International Data Corporation (IDC) already by 2010
we produced approximately 500 billion digital images through approximately a
billion devices, and this number does not include video data generated by
surveillance or for scientific use. Faced with this mindboggling capacity to
produce data about everything, everywhere and always, the fundamental question
is clearly which data are important and worth analyzing? On the one hand, our
sensors (data gathering mechanisms) will have to become more intelligent and
selective; on the other hand, we will have to develop powerful analytical tools,
analytics on a scale equivalent to the exponential increase of raw data. Now,
can we entrust this crucial task only to automatic, pre-programmed devices? A
somewhat alternative model is offered by the new collaborative forms made
possible by interconnectivity on a massive scale. For example, mass-tagging, the collective production of
hyper-glosses or hyper-annotations capable to create a meta-data compact which combines
individual opinions and computational procedures and can be mined by either
humans or programs.


Now, are this and
other forms of scholarly crowdsourcing a powerful drive toward a socialization
and democratization of research? Not necessarily, although they could be. For
instance, instead of entrusting the parsing of all the rhetorical figures used
by Dickens in his novels or by Shakespeare, Dante, Petrarch and Boccaccio in
their works, a different approach could leverage the contribution of readers
willing to tag and annotate texts through a specifically designed interface.... This method seems more suited for humanistic data mining because it is a form of
community-based research; indeed, it may contribute to the expansion of the
community of proactive readers of
literary works, and the scholarly and learning community as well. Think for
example of the possibility of comparing multi-tagging results from different
geographic and cultural or linguistic areas over time... Reader-response criticism
could indeed be revolutionized.


The question is:
which of these two methods (the one based on algorithms and the one based on
more or less massive crowdsourcing and folksonomies) is more reliable? Perhaps
the answer is: neither, taken alone, but matters could become interesting if we
find a way of intelligently combining Web 2.0 type participatory and
collaborative practices with Web 3.0 semantic tools - within a scholarly
framework.


In conclusion, I gave only one example of the kind
of critical thinking we will have to increasingly apply as humanists engaged
with a potentially radical transformation of the community we belong to: our
most daunting task is how to transpose traditional scholarly practices onto the
new platform. One thing is certain, knowledge is not structured as a tree or a
list any more, but as a graph. And isn't mass linking what scholars have always
done with different means? Perhaps it is time to start building what Paolo D'Iorio
and Michele Barbera envision: a scholarly resource
(they call it Scholarsource)
aimed to reproduce the old game of humanities research on the digital platform,
which would allow us to navigate the information tsunami by following the
relations of meaning which scholars and researchers themselves have introduced in the flux through a collaborative
metadata apparatus (primary and secondary sources, annotations, etc.), instead
of entrusting our luck every day to the anonymous albeit creative engineers of
Google & Co....


Works cited



D'Iorio, Paolo, & Barbera,
Michele. Scholarsource: A Digital Infrastructure for the
Humanities. In Thomas Bartscherer (ed.), Switching
Codes. Chicago:
Chicago University Press. 61-88.





This article is a transcription of a video presentation for the 2013 American
Association of Italian Studies annual meeting held in Eugene, Oregon. The video is embedded below and available via these links:

	Low bandwidth
	High bandwidth












