Data Citation Reprised

CC0 by Tama66 via Pixabay

So here’s a thing. A while ago, I asked whether there was any way to quantify the extent to which archaeologists were citing their reuse of data. I used the Thomson Reuters/Clarivate Analytics Data Citation Index (DCI) as a starting point, but it didn’t go too well … Back then, the DCI indicated that 56 of the 476 data studies derived from the UK’s Archaeology Data Service repository had apparently been cited elsewhere in the Web of Science databases (the figure is currently 58 out of 515). But I also found that the citations themselves were problematic: the citation of the published paper/volume was frequently incomplete or abbreviated, many appeared to be self-citations from within interim or final reports, in some cases the citations preceded the dates of the project being referenced, and in many instances it was possible to demonstrate that the data had been cited (in some form or other) but this had not been captured in the DCI. At that point I concluded that the DCI was of little value at present. So what was going on?

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Pleasing algorithms

CC0 – by Kewl via Pixabay

Social media have been the focus of much attention in recent weeks over their unwillingness/tardiness in applying their own rules. Whether it’s Twitter refusing to consider Trump’s aggressive verbal threats against North Korea to be in violation of their harassment policy, or YouTube belatedly removing a video by Logan Paul showing a suicide victim (Matsakis 2018, Meyer 2018), or US and UK government attempts to hold Facebook and Twitter to account over ‘fake news’ (e.g. Hern 2017a, 2017b), there is a growing recognition that not only are ‘we’ the data for these social media behemoths, but that these platforms are optimised for this kind of abuse (Wachter-Boettcher 2017, Bridle 2017)

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Digital proxies

proxyThe US Department of Immigration and Customs Enforcement (ICE) is apparently seeking to employ ‘big data’ methods for automating their assessment of visa applications in pursuit of meeting Trump’s calls for ‘extreme vetting’ (e.g. Joseph 2017, Joseph and Lipp 2017, and see also). A crucial problem with the proposals has been flagged in a letter to the Acting Secretary of Homeland Security by a group of scientists, engineers and others with experience in machine learning, data mining etc.. Specifically, they point to the problem that algorithms developed to detect ‘persons of interest’ could arbitrarily select groups while at the same time appearing to be objective. We’ve already seen this stereotyping and discrimination being embedded in other applications, inadvertently for the most part, and the risk is the same in this case. The reason provided in the letter is simple:

“Inevitably, because these characteristics are difficult (if not impossible) to define and measure, any algorithm will depend on ‘proxies’ that are more easily observed and may bear little or no relationship to the characteristics of interest” (Abelson et al 2017)

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The Digital Deficit

Mind the Gap
(by Christopher Brown, CC-BY 2.0)

Nicholas Carr has just pointed to some recently published research which suggests that the presence of smartphones divert our attention, using up cognitive resources which would otherwise be available for other activities, and consequently our performance on those non-phone-related activities suffers. In certain respects, this might not seem to be ‘news’ – we’re becoming increasingly accustomed to the problem of technological interruptions to our physical and cognitive activities: the way that visual and aural triggers signal new messages, new emails, new tweets arriving to distract us from the task in hand. However, this particular study was rather different.

In this case, the phones were put into silent mode so that participants would be unaware of any incoming messages, calls etc. (and if the phone was on the desk, rather than in their pocket or bag or in another room altogether, it was placed face-down to avoid any visible indicators) (Ward et al. 2017, 144). Despite this, they found that

“… the mere presence of one’s smartphone may reduce available cognitive capacity and impair cognitive functioning, even when consumers are successful at remaining focused on the task at hand” (Ward et al, 2017, 146).

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Is Digital Archaeology Busted?

And you thought you had computer problems
And you thought you had computer problems
(c) Amanda Lohr CC BY-ND 2.0 via Flickr

Timothy Brennan has just published an article in the Chronicle of Higher Education called ‘The Digital-Humanities Bust’ (behind a paywall but Google the article and on the first results page you’ll currently find a short domain link direct to the full piece). It’s a critical reflection on the state of Digital Humanities, in which he points to a decade’s worth of resources being invested in Digital Humanities, and asks what exactly they have accomplished: “To ask about the field is really to ask how or what DH knows, and what it allows us to know. The answer, it turns out, is not much.” Not surprisingly, the article has ruffled feathers amongst the Digital Humanities community, coming a year after an equally critical and hence controversial article by Allington, Brouilette and Golumbia (2016) in the LA Review of Books, ‘Neoliberal Tools (and Archives): A Political History of Digital Humanities’.

Digital Archaeology is rather different in terms of its situation (and levels of investment!). However, are there any lessons for Digital Archaeology here? If Digital Humanities are indeed a bust, is Digital Archaeology too? As an exercise, we might look at some aspects of Brennan’s diagnosis through a Digital Archaeology lens …

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Opening up Open Archaeology

openRecent years have seen a flurry of publications and statements concerning the importance and value of the open science movement in archaeology. Examples include the collection of papers published in 2012 in World Archaeology (see Lake 2012), the volume on Open Source Archaeology edited by Andrew Wilson and Ben Edwards (2015), and, most recently, a series of papers by Ben Marwick (2016; Marwick et al 2017). The idea that publications, data, and methods (including code) should be freely accessible in order to make archaeological research more reproducible is evidently a ‘good thing’ and very much in vogue.

As Tom Brughmans has recently written:

“Our very diverse work ranging from excavation, over lab tests, to interpretations is often only made available through a summarising publication that is rarely accessible to anyone other than institutions paying huge amounts of money. This is just not the way science works anymore. In such a system, how can we find out all the details of excavation results? How can we reproduce lab tests? How can we evaluate the empirical and historical background to a published interpretation in exhaustive detail? The answer is: we can’t.”

Rob Barrett has recently said something similar specifically in relation to 3D reconstruction. The value of opening up archaeological research seems undeniable, and the set of practices outlined by the new Open Science Interest Group (Marwick et al 2017, 12-13) put forward make a great deal of sense and are highly desirable. But there are some implicit underlying assumptions behind all this which don’t seem to have been addressed. They don’t detract from the importance of pursuing a truly open archaeology, but not recognising them risks not learning from past experience.

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Digitally mediated archaeologies

(CC0 – original by FunkyFocus via Pixabay)

Quartz, the digital news outlet, recently published an interview by Adrienne Matei with Peter Kahn, a psychology professor at the University of Washington. In it, they discuss how technology is affecting our lives and becoming a means to mediate the real world. The item references some of the research that Kahn and his colleagues at the Human Interaction with Nature and Technological Systems Lab (HINTS) have undertaken, aspects of which have direct relevance for understanding technology within archaeology. They raise issues such as the limitations of technological devices, questions of authenticity, changing perspectives, and what they call the ‘shifting baseline problem’, all of which have their echoes within digital archaeology.

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