The paper “Research workflows, paradata, and information visualisation: feedback on an exploratory integration of issues and practices - MEMORIA IS” (Dudek & Blaise, 2023) describes a prototype of an information system developed to improve the traceability, transmissibility and verifiability of archaeological research workflows. A key aspect of the work with MEMORIA is to make research documentation and the workflows underpinning the conducted research more approachable and understandable using a series of visual interfaces that allow users of the system to explore archaeological documentation, including metadata describing the data and paradata that describes its underlying processes. The work of Dudek and Blaise address one of the central barriers to reproducibility and transparency of research data and propose a set of both theoretically and practically well-founded tools and methods to solve this major problem. From the reported work on MEMORIA IS, information visualisation and the proposed tools emerge as an interesting and potentially powerful approach for a major push in improving the traceability, transmissibility and verifiability of research data through making research workflows easier to approach and understand.

In comparison to technical work relating to archaeological data management, this paper starts commendably with a careful explication of the conceptual and epistemic underpinnings of the MEMORIA IS both in documentation research, knowledge organisation and information visualisation literature. Rather than being developed on the basis of a set of opaque assumptions, the meticulous description of the MEMORIA IS and its theoretical and technical premises is exemplary in its transparence and richness and has potential for a long-term impact as a part of the body of literature relating to the development of archaeological documentation and documentation tools. While the text is sometimes fairly densely written, it is worth taking the effort to read it through. Another major strength of the paper is that it provides a rich set of examples of the workings of the prototype system that makes it possible to develop a comprehensive understanding of the proposed approaches and assess their validity.

As a whole, this paper and the reported work on MEMORIA IS forms a worthy addition to the literature on and practical work for developing critical infrastructures for data documentation, management and access in archaeology. Beyond archaeology and the specific context of the discussed work discussed this paper has obvious relevance to comparable work in other fields.

References

Over the last decade, a small but growing community of archaeologists, from a diversity of intellectual and demographic backgrounds, have been striving for computational reproducibility in their published research. In their survey of the accomplishments of this thriving community, Emma Karoune and Esther Plomp (2022) analyzed the wide variety of approaches researchers have taken to enhance the reproducibility of their research. A key contribution of this paper is their excellent synthesis of diverse approaches into three levels of increasing complexity. This is helpful because it provides multiple entry points for researchers new to the challenge of fortifying their research. Many researchers assume that computational reproducibility is only achievable if they have a high degree of technical skill with computers, or is only necessary if their work is very computationally intensive. Karoune and Plomp give three compelling reasons why reproducibility is important for all archaeological research, and through their three levels they demonstrate that how these levels can be accomplished with basic, non-specialized computer skills and widely used free software. They showcase exemplary work from a variety of archaeologists to show how practical and achievable reproducible research is for all archaeologists. They advocate for archaeologists to use the most widely used and supported tools and services to support their reproducible research, such as the R and Python programming languages for data analysis, and Git and GitHub for collaboration. 

This paper, with its extensive appendix including thoughtful responses to frequently asked questions about reproducible research in archaeology, is likely to have a wide reach and influence, beyond previous works on this topic that have largely focused on technical details. Karoune and Plomp have provided the on-ramp for a generation of archaeologists who will find their questions about reproducible research answered here. They will also find an agreeable entry point to reproducible research in one of the three levels described by the authors. Will every archaeologist embrace this way of working? Should they? The work of Leonelli (2018) can help us anticipate the answers to these questions. Leonelli asks where are the limits to reproducibility, and how do the characteristics of different ways of knowing affect the desirability of reproducibility? Leonelli's work invites us to consider that there will be archaeologists coming from different epistemic cultures for whom the motivations presented by Karoune and Plomp will not resonate. For example, archaeologists engaged in mostly hermeneutical social science and humanities research, who do little or no quantitative analysis and statistics, are unlikely to see reproducibility as meaningful or desirable for their work. We can describe these researchers as working in interpretative or constructivist epistemic cultures. In these cultures, the particulars of how an individual researcher engages with their subject are exclusive and unique, and they would argue it cannot be fully captured or shared in an meaningful way (Elman and Kapiszewski 2017). Here, knowledge is situational, emerging from a specific, once-off combination of people and circumstances. One example in archaeology is the chaîne opératoire approach of stone artefact analysis, which Monnier and Missal (2014:61) describe as "based upon the analyst's experience and intuition, and it is not replicable, nor quantifiable". To make sense of this example we can draw on Galison's (1997) concept of 'image traditions' and 'logic traditions'. An image tradition is a way of knowing that is qualitative, based on composing narratives from drawings and photographs. A logic tradition is based on the use of instruments and statistical methods to collect standardised quantitative data. Chaîne opératoire approaches fall into the image tradition, along with many other ways of working in archaeology that do not generate numbers or use them to support claims about the past. Archaeologists working in a logic tradition will find reproducible research to be more meaningful than those working in an image tradition.

We should be mindful not to claim that one epistemic culture is superior to another because reproducibility is not meaningful or attainable for researchers in one culture. Such a claim would threaten the plurality that is essential for the reliability of scientific knowledge (Massimi 2022). Instead we should identify those communities in archaeology where reproducible research is both meaningful and attainable, but has not yet been widely embraced. That is the where the most beneficial effects can be expected. According to Leonelli's (2018) framework, we can recognise these communities by a few basic characteristics. For example: they are doing computationally intensive archaeology, such as using or writing software to collect, simulate, analyse or visualise data; they are doing experimental archaeology; or they are making knowledge claims that are supported by tables of numeric data and data visualisations. Archaeologists whose work shares one or more of these characteristics will find the guidance provided here by Karoune and Plomp to be highly instructive and relevant, and stand the most to benefit from it.  ​​

But it is not only individual archaeological scientists that have potential to benefit from how Karoune and Plomp have lowered the barriers to reproducible research. An especially important implication of this paper is that by lowering the barriers to reproducible research, Karoune and Plomp help us all to lower barriers to participation in archaeology in general. Documenting our research transparently, and sharing our materials (such as data and code and so on) openly, can profoundly change how others can participate in archaeology. By doing this, we are enabling students and researchers elsewhere, for example in low and middle income locations, to use our materials in their teaching and learning. Other researchers and students can apply our methods to their data, and combine their data with ours to achieve syntheses beyond what a single project can do. Similarly, for archaeologists working with local, descendant or marginalized communities, the tools of reproducible research are vital for enabling community members to have full access to the archaeological process, and thus reproducibility may be considered a necessity for decolonising the discipline. Karoune and Plomp present the CARE principles (Carroll et al. 2020) to guide archaeologists in ensuring community control of data so that reproducibility can be ethically accomplished with community safety and well-being as a priority. This may have a profoundly positive impact on the demographics of archaeology, as it lowers the barriers of meaningful participation by people far beyond our immediate groups of collaborators. 

Making archaeology more accessible is of critical importance in stemming the negative social impacts of pseudoarchaeologists, who often claim that archaeologists actively suppress the truth of the archaeological record through secrecy, elitism, and exclusiveness. The harm in this is twofold. First, that pseudoarchaeology typically erases Indigenous heritage by claiming that their past achievements were due to an ancient, extinct advanced civilization, not Indigenous people. These claims are often adopted by white supremacists to support racist and antisemitic conspiracy theories (Turner and Turner 2021), which sometimes leads to prejudice, physical violence, radicalization and extremism. A second type of harm that can come from claims of secrecy and elitism is it drains public trust in experts, leading to science denial. Not only trust in archaeologists, but trust in many kinds of experts, including those working on urgent contemporary issues such as public health and climate change. Karoune and Plomp's work is important here because it provides a practical and affordable pathway for archaeologists to fight claims of secrecy and elitism by sharing their work in ways that make it possible for non-academics to inspect the analyses and logic in detail. Claims of secrecy and elitism can be easily countered by openness, transparently and reproducibility by archaeologists. This is not only useful for tackling pseudoarchaeologists, but also in enacting an ethic of care, framing members of the public as people that not only care about archaeology as part of humanity's shared heritage, but also care for the construction of reliable interpretations of the archaeological record to provide secure and authentic foundations for their social identities and relationships (Wylie et al 2018; de la Bellacasa 2011). By striving for reproducible research in the way described by Karoune and Plomp, we are practicing a kind of reciprocal care among ourselves as archaeologists, and between archaeologists and members of the public as two communities who care about the human past. 

References

Karoune, E., and Plomp, E. (2022). Removing Barriers to Reproducible Research in Archaeology. Zenodo, 7320029, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7320029

de la Bellacasa, M. P. (2011). Matters of care in technoscience: Assembling neglected things. Social Studies of Science, 41(1), 85–106. https://doi.org/10.1177/0306312710380301

Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., and Hudson, M. (2020). The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1), Article 1. https://doi.org/10.5334/dsj-2020-043​

Elman, C., and Kapiszewski, D. (2017). Benefits and Challenges of Making Qualitative Research More Transparent. Inside Higher Ed 2017,  http://web.archive.org/web/20220407064134/https://www.insidehighered.com/blogs/rethinking-research/benefits-and-challenges-making-qualitative-research-more-transparent (accessed 21 Oct, 2022). 

Galison, P. (1997). Image and logic: a material culture of microphysics. Chicago (IL): University of Chicago Press.

Leonelli, S. (2018). Re-Thinking Reproducibility as a Criterion for Research Quality [preprint]. Available online: http://philsci-archive.pitt.edu/id/eprint/14352 (Accessed 21 Oct 2022).

Massimi, M. (2022). Perspectival realism. Oxford University Press.

Monnier, G. F., and Kele M.. "Another Mousterian debate? Bordian facies, chaîne opératoire technocomplexes, and patterns of lithic variability in the western European Middle and Upper Pleistocene." Quaternary International 350 (2014): 59-83. https://doi.org/10.1016/j.quaint.2014.06.053

Turner, D. D., and Turner, M. I. (2021). “I’m Not Saying It Was Aliens”: An Archaeological and Philosophical Analysis of a Conspiracy Theory. In A. Killin and S. Allen-Hermanson (Eds.), Explorations in Archaeology and Philosophy (pp. 7–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-61052-4_2

​Wylie, C., Neeley, K., and Ferguson, S. (2018). Beyond Technological Literacy: Open Data as Active Democratic Engagement? Digital Culture & Society, 4(2), 157–182. https://doi.org/10.14361/dcs-2018-0209​​​​

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“Research perspectives and their influence for typologies” by E. Giannichedda (1) is a contribution to the upcoming volume on the role of typology and type-thinking in current archaeological theory and praxis edited by the recommenders. Taking a decidedly Italian perspective on classificatory practice grounded in what the author dubs the “history of material culture”, Giannichedda offers an inventory of six divergent but overall complementary modes of ordering archaeological material: i) chrono-typological and culture-historical, ii) techno-anthropological, iii) social, iv) socio-economic and v) cognitive. These various lenses broadly align with similarly labeled perspectives on the archaeological record more generally. According to the author, they lend themselves to different ways of identifying and using types in archaeological work. Importantly, Giannichedda reminds us that no ordering practice is a neutral act and typologies should not be devised for their own sake but because we have specific epistemic interests. Even though this view is certainly not shared by everyone involved in the broader debate on the purpose and goal of systematics, classification, typology or archaeological taxonomy (2–4), the paper emphatically defends the long-standing idea that ordering practices are not suitable to elucidate the structure and composition of reality but instead devise tools to answer certain questions or help investigate certain dimensions of complex past realities. This position considers typologies as conceptual prosthetics of knowing, a view that broadly resonates with what is referred to as epistemic instrumentalism in the philosophy of science (5, 6). Types and type-work should accordingly reflect well-defined means-end relationships.

Based on the recognition of archaeology as part of an integrated “history of material culture” rooted in a blend of continental and Anglophone theories, Giannichedda argues that type-work should pay attention to relevant relations between various artefacts in a given historical context that help further historical understanding. Classificatory practice in archaeology – the ordering of artefactual materials according to properties – must thus proceed with the goal of multifaceted “historical reconstruction in mind”. It should serve this reconstruction, and not the other way around. By drawing on the example of a Medieval nunnery in the Piedmont region of northwestern Italy, Giannichedda explores how different goals of classification and typo-praxis (linked to i-v; see above) foreground different aspects, features, and relations of archaeological materials and as such allow to pinpoint and examine different constellations of archaeological objects. He argues that archaeological typo-praxis, for this reason, should almost never concern itself with isolated artefacts but should take into account broader historical assemblages of artefacts. This does not necessarily mean to pay equal attention to all available artefacts and materials, however. To the contrary, in many cases, it is necessary to recognize that some artefacts and some features are more important than others as anchors grouping materials and establishing relations with other objects. An example are so-called ‘barometer objects’ (7) or unique pieces which often have exceptional informational value but can easily be overlooked when only shared features are taken into consideration. As Giannichedda reminds us, considering all objects and properties equally is also a normative decision and does not render ordering less subjective. The archaeological analysis of types should therefore always be complemented by an examination of variants, even if some of these variants are idiosyncratic or even unique. A type, then, may be difficult to define universally.

In total, “Research perspectives and their influence for typologies” emphasizes the need for “elastic” and “flexible” approaches to archaeological types and typologies in order to effectively respond to the manifold research interests cultivated by archaeologists as well as the many and complex past realities they face. Complexity is taken here to indicate that no single research perspective and associated mode of ordering can adequately capture the dimensionality and richness of these past realities and we can therefore only benefit from multiple co-existing ways of grouping and relating archaeological artefacts. Different logics of grouping may simply reveal different aspects of these realities. As such, Giannichedda’s proposal can be read as a formulation of the now classic pluralism thesis (8–11) – that only a plurality of ways of ordering and interrelating artefacts can unlock the full suite of relationships within historical assemblages archaeologists are interested in.

Bibliography

1. Giannichedda, E. (2023). Research perspectives and their influence for typologies, Zenodo, 7322855, ver. 9 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7322855

2. Dunnell, R. C. (2002). Systematics in Prehistory, Illustrated Edition (The Blackburn Press, 2002).

3. Reynolds, N. and Riede, F. (2019). House of cards: cultural taxonomy and the study of the European Upper Palaeolithic. Antiquity 93, 1350–1358. https://doi.org/10.15184/aqy.2019.49

4. Lyman, R. L. (2021). On the Importance of Systematics to Archaeological Research: the Covariation of Typological Diversity and Morphological Disparity. J Paleo Arch 4, 3. https://doi.org/10.1007/s41982-021-00077-6

5. Van Fraassen, B. C. (2002). The empirical stance (Yale University Press).

6. Stanford, P. K. (2006). Exceeding Our Grasp: Science, History, and the Problem of Unconceived Alternatives (Oxford University Press). https://doi.org/10.1093/0195174089.001.0001

7. Radohs, L. (2023). Urban elite culture: a methodological study of aristocracy and civic elites in sea-trading towns of the southwestern Baltic (12th-14th c.) (Böhlau).

8. Kellert, S. H., Longino, H. E. and Waters, C. K. (2006). Scientific pluralism (University of Minnesota Press).

9. Cat, J. (2012). Essay Review: Scientific Pluralism. Philosophy of Science 79, 317–325. https://doi.org/10.1086/664747

10. Chang, H. (2012). Is Water H2O?: Evidence, Realism and Pluralism (Springer Netherlands). https://doi.org/10.1007/978-94-007-3932-1

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

In this paper (Linsel/Bullenkamp/Mara 2024), the authors propose an automatic system for scar-ridge-pattern detection on palaeolithic artefacts based on Morse Theory. Scare-Ridge pattern recognition is a process that is usually done manually while creating a drawing of the object itself. Automatic systems to detect scars or ridges exist, but only a small amount of them is utilizing 3D data. In addition to the scar-ridges detection, the authors also experiment in automatically detecting the operational sequence, the temporal relation between scars and ridges. As a result, they can export a traditional drawing as well as graph models displaying the relationships between the scars and ridges.

After an introduction to the project and the practice of documenting palaeolithic artefacts, the authors explain their procedure in automatising the analysis of scars and ridges as well as their temporal relation to each other on these artefacts. To illustrate the process, an open dataset of lithic artefacts from the Grotta di Fumane, Italy, was used and 62 artefacts selected. To establish a Ground Truth, the artefacts were first annotated manually. The authors then continue to explain in detail each step of the automated process that follows and the results obtained.

In the second part of the paper, the results are presented. First the results of the segmentation process shows that the average percentage of correctly labelled vertices is over 91%, which is a remarkable result. The graph modelling however shows some more difficulties, which the authors are aware of. To enhance the process, the authors rightfully aim to include datasets of experimental archaeology in the future. They also aim to develop a way of detecting the operational sequence automatically and precisely.

This paper has great potential as it showcases exactly what Digital and Computational Archaeology is about: The development of new digital methods to enhance the analysis of archaeological data. While this procedure is still in development, the authors were able to present a valuable contribution to the automatization of analytical archaeology. By creating a step towards the machine-readability of this data, they also open up the way to further steps in machine learning within Archaeology.

Bibliography

Linsel, F., Bullenkamp, J. P., and Mara, H. (2024). Linking Scars: Topology-based Scar Detection and Graph Modeling of Paleolithic Artifacts in 3D, Zenodo, 8296269, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8296269

“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.

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