The paper entitled “IT- and machine learning-based methods of classification: The cooperative project ClaReNet – Classification and Representation for Networks” submitted by Chrisowalandis Deligio and colleagues presents the joint efforts of numismatists and data scientists in classifying a large corpus of Celtic coinage. Under the project banner “ClaReNet – Classification and Representation for Networks”, they seek to explore the possibilities and also the limits of new computational methods for classification and representation. This approach seeks to rigorously keep humans in the loop in that insights from Science and Technology Studies inform the way in which knowledge generation is monitored as part of this project. The paper was first developed for a special conference session convened at the EAA annual meeting in 2021 and is intended for an edited volume on the topic of typology, taxonomy, classification theory, and computational approaches in archaeology.

Deligio et al. (2024) begin with a brief and pertinent research-historical outline of Celtic coin studies with a specific focus on issues of classification. They raise interesting points about how variable degrees of standardisation in manufacture – industrial versus craft production, for instance – impact our ability to derive tidy typologies. The successes and failures of particular classificatory procedures and protocols can therefore help inform on the technological contexts of various object worlds and the particularities of human practices linked to their creation. These insights and discussion are eminently case-transferrable and applies not only to coinage, which after all is rather standardised, but to practically all material culture not made by machines – a point that gels particularly well with the contribution by Lucas (2022) to be published in the same volume. Although Deligio et al. do not flag this up specifically, the very expectation that archaeological finds should neatly fall into typological categories likely related to the objects that were initially used by pioneers of the method such as Montelius (1903) to elaborate its basic principles: these were metal objects often produced in moulds and very likely by only a very small number of highly-trained craftspeople such that the production of these objects approached the standardisation seen in industrial times (Nørgaard 2018) – see also Riede’s (2023) contribution due to be published in the same volume.

At the core of Deligio and colleagues’ contribution is the exploration of machine-aided classification that should, via automation, assist in handling the often large numbers of coins available in collections and, at times, from single sites or hoards. Specifically, the authors discuss the treatment of the remarkable coin hoard from Le Câtillon II on Jersey consisting of approximately 70,000 individual Celtic coins. They proceeded to employ several advanced supervised and unsupervised classification methods to process this stupendously large number of objects. Their contribution does not stop there, however, but also seeks to articulate the discussion of machine-aided classification with more theoretically informed perspectives on knowledge production. In line with similar approaches developed in-house at the Römisch-Germanische Kommission (Hofmann 2018; Hofmann et al. 2019; Auth et al. 2023), the authors also report on their cutely acronymed PANDA (Path Dependencies, Actor-Network and Digital Agency) methodology, which they deploy to reflect on the various actors and actants – humans, software, and hardware – that come together in the creation of this new body of knowledge. This latter impetus of the paper thus lifts the lid on the many intricate, idiosyncratic, and often quirky decisions and processes that characterise research in general and research that brings together humans and machines in particular – a concrete example of the messiness of knowledge production that commonly remains hidden behind the face of the published book or paper but which science studies have long pointed at as vital components of the scientific process itself (Latour and Woolgar 1979; Galison 1997; Shapin and Schaffer 2011). In this manner, the present contribution serves as inspiration to the many similar projects that are emerging right now in demonstrating just how vital a due integration of theory, epistemology, and method is as scholars are forging their path into a future where few if any archaeological projects do not include some element of machine-assistance. 

References

Auth, Frederic, Katja Rösler, Wenke Domscheit, and Kerstin P. Hofmann. 2023. “From Paper to Byte: A Workshop Report on the Digital Transformation of Two Thing Editions.” Zenodo. https://doi.org/10.5281/zenodo.8214563

Deligio, Chrisowalandis, Caroline von Nicolai, Markus Möller, Katja Rösler, Julia Tietz, Robin Krause, Kerstin P. Hofmann, Karsten Tolle, and David Wigg-Wolf. 2024. “IT- and Machine Learning-Based Methods of Classification: The Cooperative Project ClaReNet – Classification and Representation for Networks.” Zenodo, ver.5 peer-reviewed and recommended by PCI Archaeology  https://doi.org/10.5281/zenodo.7341342

Galison, Peter Louis. 1997. Image and Logic: A Material Culture of Microphysics. Chicago, IL: University of Chicago Press.

Hofmann, Kerstin P. 2018. “Dingidentitäten Und Objekttransformationen. Einige Überlegungen Zur Edition von Archäologischen Funden.” In Objektepistemologien. Zum Verhältnis von Dingen Und Wissen, edited by Markus Hilgert, Kerstin P. Hofmann, and Henrike Simon, 179–215. Berlin Studies of the Ancient World 59. Berlin: Edition Topoi. https://dx.doi.org/10.17171/3-59

Hofmann, Kerstin P., Susanne Grunwald, Franziska Lang, Ulrike Peter, Katja Rösler, Louise Rokohl, Stefan Schreiber, Karsten Tolle, and David Wigg-Wolf. 2019. “Ding-Editionen. Vom Archäologischen (Be-)Fund Übers Corpus Ins Netz.” E-Forschungsberichte des DAI 2019/2. E-Forschungsberichte Des DAI. Berlin: Deutsches Archäologisches Institut. https://publications.dainst.org/journals/efb/2236/6674

Latour, Bruno, and Steve Woolgar. 1979. Laboratory Life: The Social Construction of Scientific Facts. Laboratory Life : The Social Construction of Scientific Facts. Beverly Hills, CA: Sage.

Lucas, Gavin. 2022. “Archaeology, Typology and Machine Epistemology.” Zenodo. https://doi.org/10.5281/zenodo.7622162

Montelius, Gustaf Oscar Augustin. 1903. Die Typologische Methode. Stockholm: Almqvist and Wicksell.

Nørgaard, Heide Wrobel. 2018. Bronze Age Metalwork: Techniques and Traditions in the Nordic Bronze Age 1500-1100 BC. Oxford: Archaeopress Archaeology.

Riede, Felix. 2023. “The Role of Heritage Databases in Typological Reification: A Case Study from the Final Palaeolithic of Southern Scandinavia.” Zenodo. https://doi.org/10.5281/zenodo.8372671

Shapin, Steven, and Simon Schaffer. 2011. Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life. Princeton: Princeton University Press.