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Abstract In the 21st century, advances in computer science have impacted archaeology, most recently in the development of automated algorithms. Like most technology, these methods have been the source of ongoing debate, particularly in their utility for archaeology. Here, I focus on a contribution of automation and machine learning in archaeology that is often overlooked: the ability of computer algorithms to codify unambiguous, semantically consistent definitions. Archaeology has long struggled with establishing consistent characterizations of the phenomena it studies. As such, I argue that the procedures used for automated methods are useful for archaeologists – even outside of automated analyses – by allowing for the creation of consistent definitions which permit for reproducible research designs.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 26 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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