“Archaeology, Typology and Machine Epistemology” submitted by G. Lucas (1) offers a refreshing and welcome reflection on the role of computer-based practice, type-thinking and approaches to typology in the age of big data and the widely proclaimed ‘Third Science Revolution’ (2–4). At the annual meeting of the EAA in Maastricht in 2017, a special thematic block was dedicated to issues and opportunities linked to the Third Science Revolution in archaeology “because of [its] profound and wide ranging impact on practice and theory in archaeology for the years to come” (5). Even though the Third Science Revolution, as influentially outlined by Kristiansen in 2014 (2), has occasionally also been met with skepticism and critique as to its often implicit scientism and epistemological naivety (6–8), archaeology as a whole seems largely euphoric as to the promises of the advancing ‘revolution’. As Lucas perceptively points out, some even regard it as the long-awaited opportunity to finally fulfil the ambitions and goals of Anglophone processualism. The irony here, as Lucas rightly notes, is that early processualists initially foregrounded issues of theory and scientific epistemology, while much work conducted under the banner of the Third Science Revolution, especially within its computational branches, does not. Big data advocates have echoed Anderson’s much-cited “end of theory” (9) or at least emphatically called for an ‘empirization’ and ‘computationalization’ of theory, often under the banner of ‘data-driven archaeology’ (10), yet typically without much specification of what this is supposed to mean for archaeological theory and reflexivity. The latter is indeed often openly opposed by archaeological Third Science Revolution enthusiasts, arguably because it is viewed as part of the supposedly misguided ‘post-modernist’ project.

Lucas makes an original meta-archaeological contribution here and attempts to center the epistemological, ontological and praxeological dimensions of what is actually – in situated archaeological praxis and knowledge-production – put at stake by the mobilization of computers, algorithms and artificial intelligence (AI), including its many but presently under-reflected implications for ordering practices such as typologization. Importantly, his perspective thereby explicitly and deliberately breaks with the ‘normative project’ in traditional philosophy of science, which sought to nail down a universal, prescriptive way of doing science and securing scientific knowledge. He instead focuses on the practical dimensions and consequences of computer-reliant archaeologies, what actually happens on the ground as researchers try to grapple with the digital and the artefactual and try to negotiate new insights and knowledge, including all of the involved messiness – thereby taking up the powerful impetus of the broader practice turn in interdisciplinary science studies and STS (Science and Technology Studies (11)) (12–14), which have recently also re-oriented archaeological self-observation, metatheory and epistemology (15). This perspective on the dawning big data age in archaeology and incurred changes in the status, nature and aims of type-thinking produces a number of important insights, which Lucas fruitfully discusses in relation to promises of ‘automation’ and ‘novelty’ as these feature centrally in the rhetorics and politics of the Third Science Revolution. 

With regard to automation, Lucas makes the important point that machine or computer work as championed by big data proponents cannot adequately be qualified or understood if we approach the issue from a purely time-saving perspective. The question we have to ask instead is what work do machines actually do and how do they change the dynamics of archaeological knowledge production in the process? In this optic, automation and acceleration achieved through computation appear to make most sense in the realm of the uncontroversial, in terms of “reproducing an accepted way of doing things” as Lucas says, and this is precisely what can be observed in archaeological practice as well. The ramifications of this at first sight innocent realization are far-reaching, however. If we accept the noncontroversial claim that automation partially bypasses the need for specialists through the reproduction of already “pre-determined outputs”, automated typologization would primarily be useful in dealing with and synthesizing larger amounts of information by sorting artefacts into already accepted types rather than create novel types or typologies. If we identity the big data promise at least in part with automation, even the detection of novel patterns in any archaeological dataset used to construct new types cannot escape the fact that this novelty is always already prefigured in the data structure devised. The success of ‘supervised learning’ in AI-based approaches illustrates this. Automation thus simply shifts the epistemological burden back to data selection and preparation but this is rarely realized, precisely because of the tacit requirement of broad non-contentiousness. 

Minimally, therefore, big data approaches ironically curtail their potential for novelty by adhering to conventional data treatment and input formats, rarely problematizing the issue of data construction and the contested status of (observational) data themselves. By contrast, they seek to shield themselves against such attempts and tend to retain a tacit universalism as to the nature of archaeological data. Only in this way is it possible to claim that such data have the capacity to “speak for themselves”. To use a concept borrowed from complexity theory, archaeological automation-based type-construction that relies on supposedly basal, incontrovertible data inputs can only ever hope to achieve ‘weak emergence’ (16) – ‘strong emergence’ and therefore true, radical novelty require substantial re-thinking of archaeological data and how to construct them. This is not merely a technical question as sometimes argued by computational archaeologies – for example with reference to specifically developed, automated object tracing procedures – as even such procedures cannot escape the fundamental question of typology: which kind of observations to draw on in order to explore what aspects of artefactual variability (and why). The focus on readily measurable features – classically dimensions of artefactual form – principally evades the key problem of typology and ironically also reduces the complexity of artefactual realities these approaches assert to take seriously. The rise of computational approaches to typology therefore reintroduces the problem of universalism and, as it currently stands, reduces the complexity of observational data potentially relevant for type-construction in order to enable to exploration of the complexity of pattern. It has often been noted that this larger configuration promotes ‘data fetishism’ and because of this alienates practitioners from the archaeological record itself – to speak with Marxist theory that Lucas briefly touches upon. We will briefly return to the notion of ‘distance’ below because it can be described as a symptomatic research-logical trope (and even a goal) in this context of inquiry. 

In total, then, the aspiration for novelty is ultimately difficult to uphold if computational archaeologies refuse to engage in fundamental epistemological and reflexive self-engagement. As Lucas poignantly observes, the most promising locus for novelty is currently probably not to be found in the capacity of the machines or algorithms themselves, but in the modes of collaboration that become possible with archaeological practitioners and specialists (and possibly diverse other groups of knowledge stakeholders). In other words, computers, supercomputers and AI technologies do not revolutionize our knowledge because of their superior computational and pattern-detection capacities – or because of some mysterious ‘superintelligence’ – but because of the specific ‘division of labour’ they afford and the cognitive challenge(s) they pose. Working with computers and AI also often requires to ask new questions or at least to adapt the questions we ask. This can already be seen on the ground, when we pay attention to how machine epistemologies are effectively harnessed in archaeological practice (and is somewhat ironic given that the promise of computational archaeology is often identified with its potential to finally resolve "long-standing (old) questions"). The Third Science Revolution likely prompts a consequential transformation in the structural and material conditions of the kinds of ‘distributed’ processes of knowledge production that STS have documented as characteristic for scientific discoveries and knowledge negotiations more generally (14, 17, 18). This ongoing transformation is thus expected not only to promote new specializations with regard to the utilization of the respective computing infrastructures emerging within big data ecologies but equally to provoke increasing demand for new ways of conceptualizing observations and to reformulate the theoretical needs and goals of typology in archaeology. The rediscovery of reflexivity as an epistemic virtue within big data debates would be an important step into this direction, as it would support the shared goal of achieving true epistemic novelty, which, as Lucas points out, is usually not more than an elusive self-declaration. Big data infrastructures require novel modes of human-machine synergy, which simply cannot be developed or cultivated in an atheoretical and/or epistemological disinterested space. 

Lucas’ exploration ultimately prompts us to ask big questions (again), and this is why this is an important contribution. The elephant in the room, of course, is the overly strong notion of objectivity on which much computational archaeology is arguably premised – linked to the vow to eventually construct ‘objective typologies’. This proclivity, however, re-tables all the problematic debates of the 1960s and – to speak with the powerful root metaphor of the machine fueling much of causal-mechanistic science (19, 20) – is bound to what A. Wylie (21) and others have called the ‘view from nowhere’. Objectivity, in this latter view, is defined by the absence of positionality and subjectivity – chiefly human subjectivity – and the promise of the machine, and by extension of computational archaeology, is to purify and thus to enhance processes of knowledge production by minimizing human interference as much as possible. The distancing of the human from actual processes of data processing and inference is viewed as positive and sometimes even as an explicit goal of scientific development. Interestingly, alienation from the archaeological record is framed as an epistemic virtue here, not as a burden, because close connection with (or even worse, immersion in) the intricacies of artefacts and archaeological contexts supposedly aggravates the problem of bias. The machine, in this optic, is framed as the gatekeeper to an observer-independent reality – which to the backdoor often not only re-introduces Platonian/Aristotelian pledges to a quasi-eternal fabric of reality that only needs to be “discovered” by applying the right (broadly nonhuman) means, it is also largely inconsistent with defendable and currently debated conceptions of scientific objectivity that do not fall prey to dogma.  

Furthermore, current discussions on the open AI ChatGPT have exposed the enormous and still under-reflected dangers of leaning into radical renderings of machine epistemology: precisely because of the principles of automation and the irreducible theory-ladenness of all data, ecologies such as ChatGPT tend to reinforce the tacit epistemological background structures on which they operate and in this way can become collaborators in the legitimization and justification of the status quo (which again counteracts the potential for novelty) – they reproduce supposedly established patterns of thought. This is why, among other things, machines and AI can quickly become perpetuators of parochial and neocolonial projects – their supposed authority creates a sense of impartiality that shields against any possible critique. With Lucas, we can thus perhaps cautiously say that what is required in computational archaeology is to defuse the authority of the machine in favour of a new community archaeology that includes machines as (fallible) co-workers. Radically put, computers and AI should be recognized as subjects themselves, and treated as such, with interesting perspectives on team science and collaborative practice.

Bibliography

1. Lucas, G. (2022). Archaeology, Typology and Machine Epistemology. https:/doi.org/10.5281/zenodo.7620824.

2. Kristiansen, K. (2014). Towards a New Paradigm? The Third Science Revolution and its Possible Consequences in Archaeology. Current Swedish Archaeology 22, 11–34. https://doi.org/10.37718/CSA.2014.01.

3. Kristiansen, K. (2022). Archaeology and the Genetic Revolution in European Prehistory. Elements in the Archaeology of Europe. https://doi.org/10.1017/9781009228701

4. Eisenhower, M. S. (1964). The Third Scientific Revolution. Science News 85, 322/332. https://www.sciencenews.org/archive/third-scientific-revolution.

5. The ‘Third Science Revolution’ in Archaeology. http://www.eaa2017maastricht.nl/theme4 (March 16, 2023).

6. Ribeiro, A. (2019). Science, Data, and Case-Studies under the Third Science Revolution: Some Theoretical Considerations. Current Swedish Archaeology 27, 115–132. https://doi.org/10.37718/CSA.2019.06

7. Samida, S. (2019). “Archaeology in times of scientific omnipresence” in Archaeology, History and Biosciences: Interdisciplinary Perspectives, pp. 9–22. https://doi.org/10.1515/9783110616651

8. Sørensen, T. F.. (2017). The Two Cultures and a World Apart: Archaeology and Science at a New Crossroads. Norwegian Archaeological Review 50, 101–115. https://doi.org/10.1080/00293652.2017.1367031

9. Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/.

10. Gattiglia, G. (2015). Think big about data: Archaeology and the Big Data challenge. Archäologische Informationen 38, 113–124. https://doi.org/10.11588/ai.2015.1.26155

11. Hackett, E. J. (2008). The handbook of science and technology studies, Third edition, MIT Press/Society for the Social Studies of Science.

12. Ankeny, R., Chang, H., Boumans, M. and Boon, M. (2011). Introduction: philosophy of science in practice. Euro Jnl Phil Sci 1, 303. https://doi.org/10.1007/s13194-011-0036-4

13. Soler, L., Zwart, S., Lynch, M., Israel-Jost, V. (2014). Science after the Practice Turn in the Philosophy, History, and Social Studies of Science, Routledge.

14. Latour, B. and Woolgar, S. (1986). Laboratory life: the construction of scientific facts, Princeton University Press.

15. Chapman, R. and Wylie, A. (2016) Evidential reasoning in archaeology, Bloomsbury Academic.

16. Greve, J. and Schnabel, A. (2011). Emergenz: zur Analyse und Erklärung komplexer Strukturen, Suhrkamp.

17. Shapin, S., Schaffer, S. and Hobbes, T. (1985). Leviathan and the air-pump: Hobbes, Boyle, and the experimental life, including a translation of Thomas Hobbes, Dialogus physicus de natura aeris by Simon Schaffer, Princeton University Press.

18. Galison, P. L. and Stump, D. J. (1996).The Disunity of Science: Boundaries, Contexts, and Power, Stanford University Press.

19. Pepper, S. C. (1972). World hypotheses: a study in evidence, 7. print, University of California Press.

20. Hussain, S. T. (2019). The French-Anglophone divide in lithic research: A plea for pluralism in Palaeolithic Archaeology, Open Access Leiden Dissertations. https://hdl.handle.net/1887/69812 

21. A. Wylie, A. (2015). “A plurality of pluralisms: Collaborative practice in archaeology” in Objectivity in Science, pp. 189-210, Springer. https://doi.org/10.1007/978-3-319-14349-1_10

The article submitted by Guillaume Porraz et al. [1] shed light on the evolutionary changes recorded during the Pre-Still Bay Lynn stratigraphic unit (SU) from Diepkloof (Southern Africa). It promotes a multi-proxy and integrative approach based on a set of innovative behaviors, such as the engraving of geometric forms, silcrete heat- treatment, the use of adhesive, bladelet and bifacial tools production. This approach is not so common in Middle Stone Age (MSA) studies and makes a lot of sense for discussing the mechanisms that have fostered later innovations during the Still Bay and Howiesons Poort periods. The various innovations that emerge synchronously in this layer contrast with earlier innovations which appear as isolated phenomena in the MSA archaeological record. The strong inventiveness documented in Lynn SU is reported to a phase of experimentation for testing new ideas, new behaviors that would have played a crucial role for the emergence of the Still Bay in a context of socio-economic transformation.

The data presented in this article broadens the scope of two previous articles [2-3] based on a more representative record, collected on an area of 3,5 m² opposed to 2 m² previously, and on the first presentation and description of an engraved bone with a rhomboid pattern. Macro- and microscopic analyses together with the analysis of the distribution of the engraved lines argue convincingly for an intentional engraving. This article constitutes a key contribution to the question of HOW emerged modern cultures in Southern Africa, while calling for further research related to sites’ function, environment and local resources to address the ever-debated question of WHY the MSA groups from Southern Africa developed such unprecedented inventiveness. It makes no doubt that this article deserves recommendation by PCI Archaeology.

[1] Porraz, G., Schmidt, P., Bereiziat, G., Brugal, J.Ph., Dayet, L., Igreja, M., Miller, C.E., Viola, C., Tribolo, C., Val, A., Verna, C., Texier, P.J. 2020. Experimentation preceding innovation in a MIS5 Pre-Still Bay layer from Diepkloof Rock Shelter (South Africa): emerging technologies and symbols. 10.32942/osf.io/ch53r

[2] Porraz, G., Texier, P.J., Archer, W., Piboule, M., Rigaud, J.P, Tribolo, C. 2013. Technological successions in the Middle Stone Age sequence of Diepkloof Rock Shelter, Western Cape, South Africa. Journal of Archaeological Science 40, 3376–3400. 10.1016/j.jas.2013.02.012

[3] Porraz, G., Texier, J.P. Miller, C.E., 2014. Le complexe bifacial Still Bay et ses modalités d’émergence à l’abri Diepkloof (Middle Stone Age, Afrique du Sud). In: XXVIIème Congrès Préhistorique de France, Transitions, Ruptures et Continuité en Préhistoire. Mémoires de la Société Préhistorique Française, 155–175.

Proteomics is an increasingly applied field of study in archaeology. The characterisation of proteins in ancient biomaterials has been used extensively to determine the sex of certain animals (from dental enamel) or to identify species from non-diagnostic bone pieces or fragments of organic materials (glues and residues, for example). Paleoproteomics has been accompanied by methodological developments, in particular to reduce the size of samples affected by destructive analyses and to refine the level of species determination. The article by Joannes Dekker and colleagues (2024) - "Palaeoproteomic identification of a whale bone tool from Bronze Age Heiloo, the Netherlands" - provides a relevant and innovative example, incorporating ZooMS and SPIN techniques as well as the creation of a database of new reference collagens (cetaceans) specific to the site's natural environment (North Sea coast). The interest of this study also lies in the contribution of a use-wear analysis carried out prior to the sampling. This comparison of multidisciplinary data is essential for understanding the links between man and his natural environment and the technical and economic production that is closely linked to it. The tool studied (ca. 1500 BCE) comes from a coastal Bronze Age site in the Netherlands, where the economy was highly diversified, involving the exploitation of wild and domestic animals in both terrestrial and marine environments. The study shows that the bone of a North Atlantic right whale (Eubalaena glacialis) was shaped into a tool that was probably used to process plant fibres. This discovery supports other studies highlighting the intensive and non-opportunistic exploitation of whales in the North Sea since the Pleistocene.

Dekker, J. A. A., Mylopotamitaki, D., Verbaas, A., Sinet-Mathiot, V., Presslee, S., McCarthy, M. L., Olsen, M. T., Olsen, J. V., van den Hurk, Y., Brattinga, J. & Welker, F. (2024) Palaeoproteomic identification of a whale bone tool from Bronze Age Heiloo, the Netherlands. BioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Archaeology.  https://doi.org/10.1101/2024.04.15.589626

Anneke H. van Heteren based on reviews by Stefan Schlager and 1 anonymous reviewer

Phenomics is the analysis of high-dimensional phenotypic data [1]. Phenomics research strategies are capable of linking genetic variation to phenotypic variation [2], but a genetic component is not absolutely necessary. The paper “Archaeophenomics of ancient domestic plants and animals using geometric morphometrics: a review” by Evin and colleagues [3] examines the use of geometric morphometrics in bioarchaeology and coins the term archaeophenomics. Archaeophenomics can be described as the large-scale phenotyping of ancient remains, and both addresses taxonomic identification, as well as infers spatio-temporal agrobiodiversity dynamics. It is a relatively new field in bioarchaeology with the first paper using this approach stemming from 2004. This study by Evin et al. [3] presents an excellent review and unquestionably demonstrates the potential of archaeophenomics.

The authors provide an exhaustive review specifically of bioarchaeological studies in international journals using geometric morphometrics to study archaeological remains of domestic species. Although geometric morphometrics lends itself well for archaeophenomics, readers should keep in mind that this is not the only method and other approaches might equally fall under archaeophenomics as long as high-dimensional phenotypic archaeological data are involved.

Distinguishing archaeophenomics from phenomics is important because of a critical difference. Archaeological remains are often altered by taphonomical processes. As such data may not be as complete as when working with modern specimens. Although this poses difficulties, morphometric analyses can usually still be performed as long as the structures presenting the relevant geometrical features are present. Even fragmented remains can be studied with a restricted version of the original landmarking/measurement protocol.

Evin et al. [3] define archaeophenomics as “phenomics of the past”. This is only partly correct. It can be deduced from their review that they really mean phenomics of our (human) past. This leaves a gap for phenomics of the non-human past, for which I suggest the term palaeophenomics. 

[1] Jin, L. (2021). Welcome to the Phenomics Journal. Phenomics, 1, 1–2. https://doi.org/10.1007/s43657-020-00009-4.
[2] Bilder, R.M., Sabb, F.W., Cannon, T.D., London, E.D., Jentsch, J.D., Stott Parker, D., Poldrack, R.A., Evans, C., Freimer, N.B. (2009). Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience, 164(1), 30-42. https://doi.org/10.1016/j.neuroscience.2009.01.027
[3] Evin, A., Bouby, L., Bonhomme, V., Jeanty, A., Jeanjean, M., Terral., J.-F. (2022). Archaeophenomics of ancient domestic plants and animals using geometric morphometrics: a review. Peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.31219/osf.io/skeu5

The nature of breaking long bones for obtaining marrow is important in Paleolithic archaeology, due to its widespread, almost universal, character. Provided that hammer-stone percussion marks can be correctly identified using experimental datasets (e.g., [1]), the anatomical location and count of the marks may be taken to reflect recurrent “cultural” traditions in the Paleolithic [2]. Were MP humans breaking bones intuitively or did they abide by a strict “protocol”, and, if the latter, was this protocol optimized for marrow retrieval or geared towards another, less obvious goal?
The new preprint by Vettese and colleagues [3] took a novel approach to this question, by conducting an experiment which involved novice butchers, relying on nothing but their intuition, and recording the way in which they broke marrow-rich bones. Some variability was noted in the “intuitive” patterns; indeed, future studies replicating this experiment and adding more variables such as more experienced butchers and non-bovid bones are warranted. Similarities in the means by which novice butchers break bones was also observed, and especially telling is the strong effect of anatomical features in most bones, except for the femur.

This paper provides a baseline for location analyses of percussion marks. Their dataset may therefore be regarded as a null hypothesis according to which the archaeological data could be tested. If Paleolithic patterns of percussion marks differ from Vettese et al.’s [3] “intuitive” patterns, then the null hypothesis is disproved and one can argue in favor of a learned pattern. The latter can be a result of ”culture”, as Vettese et al. [3] phrase it, in the sense of nonrandom action that draws on transmitted knowledge. Such comparisons bear a great potential for understanding the degree of technological behavior in the Paleolithic by factoring out the “natural” constraints of bone breakage patterns. Vettese et al. [3: fig. 14] started this discourse by comparing their experimental dataset to some Middle and Upper Paleolithic faunas; we are confident that many other studies will follow.

Bibliography

[1]Pickering, T.R., Egeland, C.P., 2006. Experimental patterns of hammerstone percussion damage on bones: Implications for inferences of carcass processing by humans. J. Archaeol. Sci. 33, 459–469. https://doi.org/10.1016/j.jas.2005.09.001

[2]Blasco, R., Rosell, J., Domínguez-Rodrigo, M., Lozano, S., Pastó, I., Riba, D., Vaquero, M., Peris, J.F., Arsuaga, J.L., de Castro, J.M.B., Carbonell, E., 2013. Learning by Heart: Cultural Patterns in the Faunal Processing Sequence during the Middle Pleistocene. PLoS One 8, e55863. https://doi.org/10.1371/journal.pone.0055863

[3]Vettese, D., Stavrova, T., Borel, A., Marin, J., Moncel, M.-H., Arzarello, M., Daujeard, C. (2020) A way to break bones? The weight of intuitiveness. BioRxiv, 011320, ver. 4 peer-reviewed and recommended by PCI Archaeology. https://doi.org/10.1101/2020.03.31.011320