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https://ceur-ws.org/Vol-1878/article-05.pdf
Scholarly Social Machines
David De Roure 1,š
1Oxford e-Research Centre, University of Oxford, Oxford, UK
šdavid.deroure@oerc.ox.ac.uk
Identiļ¬er: http://www.oerc.ox.ac.uk/sites/default/ļ¬les/users/user384
/scholarly-social-machines.html
In Reply To: https://linkedresearch.org/calls
Despite many attempts to perturb a scholarly publishing system that is over
350 years old, it feels pretty much like business as usual.[1] Here I question
whether we have become trapped inside the machine, and argue that if we
want to change anything in an informed way then we need to step outside and
take a look. How do we do this? First I describe what I mean by a social maā
chine, and the āscholarly social machines ecosystemā. The article closes with a
list of questions that I believe we need to be asking.
The evolutionary growth of new social engines
Once upon a time, interacting with digital content was an option, as was turnā
ing to social networking sites to communicate with friends and colleagues. Toā
day our lives are mandatorily mediated by technology that enables academic,
social, economic and cultural interactions at scale. Our widespread adoption of
Web, laptop and smartphone, with many more devices still to come, means we
ļ¬nd ourselves living in interleaved physical and virtual worlds.
The design and analysis of these socio-technical systems has attracted much
academic attention, exploring both social science and computer science perā
spectives. Here we focus on one model in particular, because it is an abstracā
tion that underpins the Webāit is the Social Machine.
Tim Berners-Lee provides a deļ¬nition of Social Machines in his book Weavā
ing the Web:[2] āprocesses in which the people do the creative work and the
machine does the administrationā. A less quoted but more complete deļ¬nition
follows in the same passage: āThe stage is set for an evolutionary growth of
new social engines. The ability to create new forms of social process would be
given to the world at large, and development would be rapid.ā Written in
1999, Berners-Lee was already anticipating the āsocial enginesā like Wikipedia
and twitter that were to follow over several years.
SOCIAM
In 2012 a consortium of UK universities embarked on the SOCIAM project,
its ļ¬ve year mission to explore The Theory and Practice of Social Machines.
The SOCIAM team started their journey by identifying individual social maā
chines to study, and ā like true explorers ā endeavouring to identify and cateā
gorise the social machines that were out there in the jungle. For example,
Wikipedia became a popular embodiment of the notion of the Social Machine
ā an open platform, operating at scale, widely known and observed, clearly soā
cially constituted, and complete with a crowd and automation.
The SOCIAM project went on to study many others, and especially Zooniā
verse, which evolved from the Galaxy Zoo citizen science site into a kind of soā
cial machine factory. In its latest incarnation itās a platform that empowers
citizens to create their own social machines ā recalling that empowerment unā
derpins that original deļ¬nition.
Signiļ¬cantly, Zooniverse represents a new way of conducting scholarship, exā
ploiting the new aļ¬ordances of the digital, especially scale, automation and
empowerment. Can our knowledge infrastructure[3] cope with this shift in
scholarship? We return to this question later.
The Scholarly Social Machines Ecosystem
Studying individual machines is clearly important in order to understand how
to build them. But over time citizens typically engage with more than one soā
cial machine, and really we have a socially-coupled ecosystem of social maā
chines. We need to understand this ecosystem: as designers we are not really
creating standalone social machines, but making an intervention in the ecosysā
tem with an intended outcome in mind (and what happens might be comā
pletely diļ¬erent).
Interested in the ecosystem angle from the outset, I observed the auto-
ethnographic opportunity: we are all engaged in a āscholarly social machinesā
ecosystem. We author, review and publish; we generate born-digital content
and repositories to put it in; we discover and read and recommend; we crowdā
source our research and we engage the public. Also we use software for our reā
search, and this lives in an adjacent region of the ecosystem, the land with
github, stack overļ¬ow and other social machines coupled by developers and reā
search software engineers.
In scholarly communications, traditional centralised monolithic processes
looked set to give way to a vibrant ecosystem of new intermediaries throughā
out the research lifecycle and for every aspect of communication ā and sigā
niļ¬cantly they are available for us (and our service providers) to select and to
assemble, joined up by DOIs, APIs and ORCIDs. For me it is this very ability
to assemble, reconļ¬gure and repurpose social machines that makes them disā
tinctive in the landscape of sociotechnical models. This is not to say that
scholarly social machines need to be mediated by IT: we have also looked at a
historical perspective, not just pre-web but early modern.[4]
So I made an early slide with the logos of various tools, websites, platforms
and publishers that were being promoted at events like the FORCE conferā
ences. Since then Iāve spotted many similar slides ā but the logos change
quickly, because this ecosystem is quite dynamic, and natural selection is at
play. We see disintermediation and new intermediaries, at various granulariā
ties. And we see historical intermediaries, like publishers, acting to avoid disinā
termediationāI once called this phenomenon āantidisintermediationarianismā.
Trapped inside the machine
Social machines give us a lens and an opportunity for academic insight into a
vital ecosystem, but in practice this ecosystem hasnāt attracted the attention I
hoped. Reļ¬ecting on this, I think it might be precisely because we are inside
the machine and ļ¬nd it hard to step outside and take a look at ourselves.
We still talk in traditional terms. We talk about data but forget about softā
ware. We donāt discuss how citizen science doesnāt ļ¬t very well. When time or
resource for change is limited, everything looks like open access (yet again)
and the parallel world of open data that has been invented to mimic it. We
forget about cultural publishing diļ¬erences and look for one size ļ¬ts all. And
we write yet more reports that say pretty much the same things.
And then thereās the Catch-22: the way we try to tackle the problem is to
use traditional publishing, to use the very machines that we believe are ļ¬awed.
For example, you are (probably) reading this article in the existing social maā
chine (and if youāre not then congratulations, you escaped! And I oļ¬er you
this piece as a historical artifact from an uncertain time, with an uncertain arā
chive, and congratulate you on a miracle of preservation and discovery).
And then there are the antidisintermediationists, who would rather everyone
stayed inside publishing as we know it, seducing us with the familiarity of a
revamped status quo instead of a radical rethink.
The view from outside
But I believe we must step outside, because as long as we are inside we are
not asking important and hard bigger questions. Here are some examples:
1. We know that the real-time data supply to our research is going to increase
dramatically, but have we really thought about what this will do to the
ecosystem? Is our knowledge infrastructure ready? Have we rehearsed the
methods, and if so where?
2. Can we achieve the full potential of shifts in scholarship, such as citizen sciā
ence and its augmentation through machine learning, and facilitate rather
than constrain further innovation?
3. Are our teams ready? Can you tell me what research team sizes we will be
working with in the future? What specialists do we need? How restricted are
we by disciplinary silos?
4. How much will be automated? What percentage of academic content will be
produced by machine? Consumed by machine?
5. Which components and processes will become obsolete? Are we ready to reā
place rather than revamp? Will policy interventions be eļ¬ective, and will they
have unexpected side eļ¬ects? e.g. What percentage of publications need to
comply with FAIR or data citation principles to have a useful eļ¬ect?
6. And once we ļ¬gure out what we need to do, how do we ļ¬gure out the best
interventions to achieve it?
7. How do we use social machines as an abstraction that helps describe, underā
stand, analyse, and model the scholarly social machines ecosystem?
8. And ļ¬nally, how do we evidence the optimum granularities in our scholarly
communications ecosystem on the spectrum between extreme decentralisation,
which aims to empower the individual and community, and massive monolithic
social platforms which harness collective energies to beneļ¬t a smaller conā
stituency?
Perhaps it will help if we look at a diļ¬erent ecosystem and then turn round
and look back at ours. The software ecosystem is an excellent exemplar but
still quite close. So I oļ¬er the social machines of music: downloads, streaming ,
music recognition, music publishing, uploads, fandom. Why is this relevant?
Well for one thing, the music industry has āgone digitalā end to (nearly) end,
in a way that science still aspires to.[5] Itās about planning, performance,
recording, production, distribution, discovery, delivery, consumption and reuse.
Itās about creativity, and fundamentally itās about people.
Acknowledgements
I am grateful to many colleagues who have engaged in discussions about scholā
arly social machines, including Dave Murray-Rust, SĆ©golĆØne Tarte, Pip Willā
cox, and the participants in the Social Humanities workshop at the Digital
Humanities Oxford Summer School 2016.
This article is a response to the Call for Linked Research.
References
1. De Roure, D., (2014). The future of scholarly communications. Insights.
27(3), pp.233ā238. doi: 10.1629/2048-7754.171
2. Tim Berners-Lee, Mark Fischetti. 1999. Weaving the Web: The Original Deā
sign and Ultimate Destiny of the World Wide Web by its Inventor (1st ed.).
Harper San Francisco.
3. Edwards, P. N., Jackson, S. J., Chalmers, M. K., Bowker, G. C., Borgman,
C. L., Ribes, D., Burton, M., & Calvert, S. (2013) Knowledge Infrastructures:
Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue.
http://hdl.handle.net/2027.42/97552.
4. David De Roure and Pip Willcox (2015). Coniunction, with the participaā
tion of Society: Citizens, Scale, and Scholarly Social Machines. Scholarly
Communications Workshop, Boston, MA. April 2015. Available on
http://www.academia.edu/12103878/
5. David De Roure, Graham Klyne, Kevin R. Page, John Pybus, David M.
Weigl, Matthew Wilcoxson, Pip Willcox (2016). Plans and performances: Parā
allels in the production of science and music. 2016 IEEE 12th International
Conference on e-Science, Baltimore, MD, 2016, pp. 185-192. doi:
10.1109/eScience.2016.7870899