Year: 2016

Back in April, I gave a talk at a symposium entitled Finding New Knowledge: Archival Records in the Age of Big Data in Maryland called “Of what are they a source? The Crowd as Authors, Observers and Meaning-Makers”. In this talk I made the point that 2016 marked ten years since Jeff Howe coined the term “crowdsourcing” as a pastiche of “outsourcing” in his now-famous Wired piece. I also talked about the saga of “Boaty McBoatface”, then making headlines in the UK. If you recall, Boaty McBoatface was the winner, with over 12,000 votes, of the Natural Environmental Research Council’s open-ended appeal to “the crowd” to suggest names for its new £200m polar research ship, and vote on the suggestions. I asked if the episode had anything to tell us about where crowdsourcing had gone in its first ten years.  Well, we had a good titter at poor old NERC’s expense (although in fairness I did point out that, in a way, it was wildly successful as a crowdsourcing exercise – surely global awareness of NERC’s essential work in climatology and polar research has never been higher). In my talk I suggested the Boaty McBoatface episode was emblematic of crowdsourcing in the hyper-networked age of social media. The crowdsourcing of 2006 was based, yes, on networks, enabled by the emerging ubiquity of the World Wide Web, but it was a model where “producers” – companies with T-Shirts to design (Howe’s example), astrophysicists with galaxy images to classify (the Zooniverse poster child of citizen science), or users of Amazon Mechanical Turk put content online, and entreated “the crowd” to do something with it. This is interactivity at a fairly basic level. But the 2016 level of web interactivity is a completely different ball game, and it is skewing attitudes to expertise and professionalism in unexpected and unsettling ways.

The relationship between citizen science (or academic crowdsourcing) and “The Wisdom of Crowds” has always been a nebulous one. The earlier iterations of Transcribe Bentham, for example, or Old Weather, are not so much exercises in crowd wisdom, but perhaps “crowd intelligence” – the execution of intelligent tasks that a computer could not undertake. These activities (and the numerous others I examined with Mark Hedges in our AHRC Crowd-Sourcing Scoping Survey four years) ago all involve intelligent decision making, even if it is simply an intelligent decision as to how a particular word in Bentham’s papers should be transcribed. The decisions are defined and, to differing degrees, constrained by the input and oversight of expert project members, which give context and structure to those intelligent decisions: a recent set of interviews we have conducted with crowdsourcing projects have all stressed the centrality of a co-productive relationship between professional project staff and non-professional project participants (“volunpeers”, to use the rather wonderful terminology of the Smithsonian Transcription Center’s initiative).

However events since April have put the relationship between “the crowd” and “the expert” on to the front pages on a fairly regular basis. Four months ago, the United Kingdom voted by the small but decisive margin of 51.9% to 48.1% to exit the European Union. The “Wisdom of [the] Crowd” in making this decision informed much of the debate in the run up to the vote, with the merits of “crowd wisdom” versus “expert wisdom” being a key theme. Michael Gove, a politician who turned out to be too treacherous even for a Conservative party leadership election, famously declared that “Britain has had enough of experts”. It is a theme that has persisted since the vote, placing the qualification obtained from the act of representing “ordinary people” through election directly over, say, the economic expertise of the Governor of the Bank of England:

Is this fault line between the expert and the crowd real, a social division negotiated by successful academic crowdsourcing projects, or is it merely a conceit of divisive political rhetoric?  Essentially, this is a question of who “produces” wisdom, and who “consumes” it, and in which direction do the cognitive processes which lead to decision making flow (and which way should they flow?). This highlights the nebulous and inexact definition of “the crowd”. It worked pretty well ten years ago when Howe wrote his article, and translated easily enough into the “crowd intelligence” paradigm of the late 2000s, and early academic crowdsourcing. In these earlier days of Web 2.0, it was still possible to make at least a scalar distinction between producers and consumers, between the crowd and the crowdsourcer (or the outsourcer and organization outsourced to, to keep with his metaphor); even though the role of the user as a creator and a consumer of content was changing (2006 was, after all, also the year in which Facebook and Twitter launched). But how about today? This is a question raised by a recent data analysis of Brexit by the Economist. In this survey of voters’ opinions, it emerges that over 80% of Leave voters stated that they had “more faith in the wisdom of ordinary people than the opinions of experts”. I find the wording of this question fascinating, if not a little loaded – after all, is it not reasonable to place one’s faith in any kind of “wisdom” than an “opinion”? But the implicit connection between generally a generally held belief and (crowd) wisdom is antithetical to independent decision making. This is crucial to any argument that “crowd wisdom” leads to better decisions – such as leaving the EU. In his 2004 book, The Wisdom of Crowds: Why the Many Are Smarter Than The Few, James Surowiecki talks of “information cascades” being a threat to good crowd decisions. In information cascades, people rely on ungrounded opinions of others that have gone before: the more opinions, the more ongoing, self-replicating reinforcement. Surowiecki says:

Independence is important to intelligent decision making for two reasons. First, it keep (sic) the mistakes that people make from becoming correlated … [o]ne of the quickest ways to make people’s judgements systematically biased is to make them dependent on each other for information. Second, independent individuals are more likely to have new information rather than the same old data everyone is already familiar with.

According to the Economist’s data, the Brexit vote certainly has some of the characteristics of information cascade as described by Surowiecki: many of those polled who voted that way did so at least in part of their faith in the “wisdom of ordinary people”. This is the same self-replicating logic of the NERC boat naming competition which led to Boaty McBoatface; and a product of the kind of closed-loop thinking which social media represents. Five years ago, the New Scientist reported a very similar phenomenon with different kinds of hashtags – depending on the kind of community involved, some (#TeaParty in their example) develop great traction among distinct groups of mutual followers with individuals tweeting to one another, whereas others (#OccpyWallStreet in this case) attract much greater engagement from those not already engaged. It’s a pattern that comes up again and again, and surely Brexit is a harbinger of new ways in which democracy works.

It is certainly embodies and represents the information cascade as one key aspect that Surowiecki would have us believe is not the Wisdom of Crowds as a means of making “good” decisions. There may be those who say that to argue this is to argue against democracy, that there are no “good” or “bad” decisions, only “democratic” ones.  That is completely true of course; and not for a moment here do I question the democratic validity of the Brexit decision itself. I also happen to believe that millions of Leave voters are decent, intelligent, honourable people who genuinely voted for what, in their considered opinion, was the best for the country. But since the Goves of the world made a point and a virtue of placing the Leave case in opposition to the “opinions of experts”, it becomes legitimate to ask questions about the cognitive processes which result from so doing. And the contrast of this divisive rhetoric with those constructive and collaborative relationships between experts and non-experts evident from academic crowdsourcing could not be greater.

But that in turn makes one ask how useful the label “expert” really is. What, in the rhetoric of Gove, Davies etc, actually consigns any individual person to this reviled category? Is it just anyone who works in a university or other professional organization? Who is and who is not an expert is a matter of circumstance and perspective, and it shifts and changes all the time. Those academic crowdsourcing projects understand that, which is why they were so successful. If only politics could take the lesson.

 

This summer, there was an extensive discussion on the Humanist mailing list about the form and nature of research methods in digital humanities. This matters, as it speaks in a fundamental way to a question whose very asking defines Digital Humanities as a discipline: when does the development and use of a tool become a method, or a methodology? The thoughts and responses this thread provoked is testament to the importance of this question.  While this post does not aim to offer a complete digest of this thread, I wanted to highlight a couple of key points that emerged from it. A key theme was in one exchange, which concerned the point in any research activity which employs digital tools at which human interpretation enters. Should this be the creation of tools, the design of those tools, the adding of metadata, the design of metadata, and so on. If one is creating a set of metadata records relating to a painting with reference to “Charles I” (ran an example given by Dominic Oldman), the computer would not “understand” the meaning of any information provided by the user, and any subsequent online aggregation would be similarly knowledge-agnostic.

In other words, where should human knowledge in the Digital Humanities lie? In the tool, or in the data, or both?

Whatever the answer, the key aspect is the point at which a convention in the use of a particular tool becomes a method. In a posting to the thread on 25^th July, Willard McCarty stated:

The divergence is over the tendency of ‘method’ to become something fixed. (Consider, for example, “I have a method for doing that.” Contrast “What if I try doing this?”).

“Fixedness” is essential, and it implies some form of critically-grounded consensus among those using the method in question. This is perhaps easier to see in the social sciences that it is in the [Digital] humanities. For example, how would a classicist, or an historian, or a literature scholar approaching manuscripts through the method of close reading present and describe that method in the appropriate section of the paper? How would this differ from, say the equivalent section in a paper by a social scientist using grounded theory to approach a set of interviews? While there may be no differentiation in the rigour or quality of the research, but one suspects the latter would have a far greater consensus – and body of methodological literature – to draw upon to describe grounded theory, than the former would to describe close reading.

Many discussions on this subject remain content-focused still. What content means in itself has assumed a broader aspect. Whereas “content” in the DH may once have meant digitized texts, images and manuscripts, surely now it also includes web content such as tweets, transient social media, and blog posts such as this one. It is essential to continue to address the DH research life-cycle, as based on content, but I still but we need to tackle explicitly methodology (emphasis deliberate), in both its definition and epistemology, and defined by the presence of fixity, as noted by McCarty.” Methodological pluralism”, the key theme of the thread on Humanist this summer, is great, but for there to be pluralism, there must first be singularity. As noted, the social sciences have this in a very grounded way. I have always argued that the very terms “quantitative” and “qualitative” are understood, shared, written about and, ultimately, used in a much more systematic way in the social sciences than in the (digital) humanities, where they are often taken to express a simple distinction between “something than can be computed versus something that cannot”.

I am not saying this is not a useful distinction, but surely the Humanist thread shows that the DH should at least deepen the distinction to mean “something which can be understood by a computer versus something that cannot”.

I would like to pose three further questions on the topic:

1) how are “technological approaches” defined in DH – e.g. the use of a tool, the use of a suite of tools, the composite use of a generic set of digital applications?

2) what does a “technological approach” employing one or more tools enable us to do?

3) how is what we do with technology a) replicable and b) documentable?

Pencil sketch, mostly harder pencils. August 2016.