Filter bubbles

In an earlier post I wrote about the importance of understanding the legibility, agency and negotiability of archaeological data as we increasingly depend on online data delivery as the basis for the archaeologies we write and especially as those archaeologies show signs of being partly written by the delivery systems themselves.

A simple illustration of this is the idea of filter bubbles. This term was coined in 2011 by Eli Pariser to describe the way in which search algorithms selectively return results depending on their knowledge of the person who asked the question. It’s an idea previously flagged by, amongst others, Jaron Lanier who wrote about ‘agents of alienation’ in 1995, but it came to the fore through the recognition of the personalisation of Google results and Facebook feeds (and is the counter-selling point of the alternative search engine, DuckDuckGo, for example). So can we see this happening with archaeological data? Perhaps not to the extent described by Pariser, Lanier and others, but still …

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Big Data Analytics

It was only a matter of time before a ‘big data’ company latched onto archaeology for commercial purposes. Reported in a New Scientist article last week (with an unfortunate focus on ‘treasure’), a UK data analytics start-up called Democrata is incorporating archaeological data into a system to allow engineering and construction firms to predict the likelihood of encountering archaeological remains. This, of course, is what local authority archaeologists do, along with environmental impact assessments undertaken by commercial archaeology units. But this isn’t (yet) an argument about a potential threat to archaeological jobs.

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Open Data quality

In relation to the Portable Antiquities Scheme (PAS) database, David Gill on his ‘Looting Matters’ blog has pondered “How far can we trust the information supplied with the reported objects? Are these largely reported or ‘said to be’ findspots?”.

Spatial information is frequently cited as a problem in relation to open archaeological data – but the focus tends to be on the risks it poses for looting (for example, Bevan 2012, 7-8; Kansa 2012, 508-9).

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