“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 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. https://doi.org/10.5281/zenodo.13304085

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.

Data authenticity and approaches to data authentication are crucial issues in ancient protein research. The advent of modern mass spectrometry has enabled the detection of traces of ancient biomolecules contained in fossils, including protein sequences. However, detecting proteins in ancient samples does not equate to demonstrating their endogenous nature: instead, if the mechanisms that drive protein preservation and degradation are understood, then the extent of protein diagenesis can be used for evaluating preservational quality, which in turn may be related to the authenticity of the protein data. 

The post-mortem deamidation of asparaginyl and glutamyl residues is a key degradation reaction, which can be assessed effectively on the basis of mass spectrometry data, and which has accrued a long history of research, both in terms of describing the mechanisms governing the reactions and with regard to the best strategies for assessing and quantifying the extent of glutamine (Gln) and asparagine (Asn) deamidation in ancient samples (Pal Chowdhury et al., 2019; Ramsøe et al., 2021, 2020; Schroeter and Cleland, 2016; Simpson et al., 2016; Solazzo et al., 2014; Welker et al., 2016; Wilson et al., 2012). 

In their paper, Nair and colleagues (2022) build on this wealth of knowledge and present a tool for quantifying the extent of Gln deamidation in parchment. Parchment is a collagen-based material which can yield extraordinary insights into manuscript manufacturing practices in the past, as well as on the daily lives of the people who assembled and used them (“biocodicology”) (Fiddyment et al., 2021, 2019, 2015; Teasdale et al., 2017). Importantly, the extent of deamidation can be directly related to the quality of the parchment produced: rapid direct deamidation of Gln is induced by the liming process, therefore high extents of deamidation are linked to prolonged exposure to the high pH conditions which are typical of liming, thus implying lower-quality parchment.

Nair et al.’s approach focuses on collagen peptides which are typically detected during MALDI-TOF mass spectrometry analyses of parchment and build a simple three-step workflow able to yield an overall index of deamidation for a sample (the parchment glutamine index - PQI) 一 taking into account that different Gln residues degrade at different rates according to their micro-chemical environment. The first step involves pre-processing the MALDI spectra, since Nair et al. are specifically interested in maximising information which can be obtained by low-quality data. The second step builds on well-established methods for quantifying Q → E from MALDI-TOF data by modelling the convoluted isotope distributions (Wilson et al., 2012). Once relative rates of deamidation in selected peptides within a given sample are calculated, the third step uses a mixed effects model to combine the individual deamidation estimates and to obtain an overall estimate of the deamidation for a parchment sample (PQI). 

The PQI can be used effectively for assessing parchment quality, as the authors show for the dataset from Orval Abbey. However, PQI could also have wider applications to the study of processed collagen, which is widely used in the food and pharmaceutical industries. In general, the study by Nair et al. is a welcome addition to a growing body of research on protein diagenesis, which will ultimately improve models for the assessment of the authenticity of biomolecular data in archaeology. 

References

Chowdhury, P.M., Wogelius, R., Manning, P.L., Metz, L., Slimak, L., and Buckley, M. 2019. Collagen deamidation in archaeological bone as an assessment for relative decay rates. Archaeometry 61:1382–1398. https://doi.org/10.1111/arcm.12492

Fiddyment, S., Goodison, N.J., Brenner, E., Signorello, S., Price, K., and Collins, M.J.. 2021. Girding the loins? Direct evidence of the use of a medieval parchment birthing girdle from biomolecular analysis. bioRxiv. https://doi.org/10.1098/rsos.202055

Fiddyment,S., Holsinger, B., Ruzzier, C., Devine, A., Binois, A., Albarella, U., Fischer, R., Nichols, E., Curtis, A., Cheese, E., Teasdale, M.D., Checkley-Scott, C., Milner, S.J., Rudy, K.M., Johnson, E.J., Vnouček, J., Garrison, M., McGrory, S., Bradley, D.G., and Collins, M.J. 2015. Animal origin of 13th-century uterine vellum revealed using noninvasive peptide fingerprinting. Proc Natl Acad Sci U S A 112:15066–15071. https://doi.org/10.1073/pnas.1512264112

Fiddyment, S., Teasdale, M.D., Vnouček, J., Lévêque, É., Binois, A., and Collins, M.J. 2019. So you want to do biocodicology? A field guide to the biological analysis of parchment. Heritage Science 7:35. https://doi.org/10.1186/s40494-019-0278-6

Nair, B., Rodríguez Palomo, I., Markussen, B., Wiuf, C., Fiddyment, S., and Collins, M. Parchment Glutamine Index (PQI): A novel method to estimate glutamine deamidation levels in parchment collagen obtained from low-quality MALDI-TOF data. BiorRxiv, 2022.03.13.483627, ver. 6 peer-reviewed and recommended by Peer community in Archaeology. https://doi.org/10.1101/2022.03.13.483627 

Ramsøe, A., Crispin, M., Mackie, M., McGrath, K., Fischer, R., Demarchi, B., Collins, M.J., Hendy, J., and Speller, C. 2021. Assessing the degradation of ancient milk proteins through site-specific deamidation patterns. Sci Rep 11:7795. https://doi.org/10.1038/s41598-021-87125-x

Ramsøe, A., van Heekeren, V., Ponce, P., Fischer, R., Barnes, I., Speller, C., and Collins, M.J. 2020. DeamiDATE 1.0: Site-specific deamidation as a tool to assess authenticity of members of ancient proteomes. J Archaeol Sci 115:105080. https://doi.org/10.1016/j.jas.2020.105080

Schroeter, E.R., and Cleland, T.P. 2016. Glutamine deamidation: an indicator of antiquity, or preservational quality? Rapid Commun Mass Spectrom 30:251–255. https://doi.org/10.1002/rcm.7445

Simpson, J.P., Penkman, K.E.H., and Demarchi, B. 2016. The effects of demineralisation and sampling point variability on the measurement of glutamine deamidation in type I collagen extracted from bone. J Archaeol Sci 69: 29-38. https://doi.org/10.1016/j.jas.2016.02.002

Solazzo, C., Wilson, J., Dyer, J.M., Clerens, S., Plowman, J.E., von Holstein, I., Walton Rogers, P., Peacock, E.E., and Collins, M.J. 2014. Modeling deamidation in sheep α-keratin peptides and application to archeological wool textiles. Anal Chem 86:567–575. https://doi.org/10.1021/ac4026362

Teasdale, M.D., Fiddyment, S., Vnouček, J., Mattiangeli, V., Speller, C., Binois, A., Carver, M., Dand, C., Newfield, T.P., Webb, C.C., Bradley, D.G., and Collins M.J. 2017. The York Gospels: a 1000-year biological palimpsest. R Soc Open Sci 4:170988. https://doi.org/10.1098/rsos.170988

Welker, F., Soressi, M.A., Roussel, M., van Riemsdijk, I., Hublin, J.-J., and Collins, M.J. 2016. Variations in glutamine deamidation for a Châtelperronian bone assemblage as measured by peptide mass fingerprinting of collagen. STAR: Science & Technology of Archaeological Research 3:15–27. https://doi.org/10.1080/20548923.2016.1258825

Wilson, J., van Doorn, N.L., and Collins, M.J. 2012. Assessing the extent of bone degradation using glutamine deamidation in collagen. Anal Chem 84:9041–9048. https://doi.org/10.1021/ac301333t

After a thorough review and consideration of the revised manuscript titled "Cultural Significance Assessment of Archaeological Sites for Heritage Management: From Text to Spatial Networks of Meanings" by Yael Alef and Yuval Shafriri [1], I am recommending the paper for publication. The authors have made significant strides in addressing the feedback from the initial review process, notably enhancing the manuscript's clarity, methodological detail, and overall contribution to the field of Archaeological Resource Management (ARM).

On balance I think the paper competently navigates the shift from a traditional significance-focused assessment of isolated archaeological sites to a more holistic and interconnected approach, leveraging graph data models and spatial networks. This transition represents an advancement in the field, offering deeper insights into the sociocultural dynamics of archaeological sites. The case study of ancient synagogues in northern Israel, particularly the Huqoq Synagogue, serves as a compelling illustration of the potential of semantic technologies to enrich our understanding of cultural heritage.

Significantly, the authors have responded to the call for a clearer methodological framework by providing a more detailed exposition of their use of knowledge graph visualization and semantic technologies. This response not only strengthens the paper's scientific rigor but also enhances its accessibility and applicability to a broader audience within the conservation and heritage management community.

However, I do think it remains important to acknowledge areas where further work could enrich the paper's contribution. While the manuscript makes notable advancements in the technical and methodological domains, the exploration of the ethical and political implications of semantic technologies in ARM remains less developed. Recognizing the complex interplay of ethical and political considerations in archaeological assessments is crucial for the responsible advancement of the field. Thus, I suggest that future work could productively focus on these dimensions, offering a more comprehensive view of the implications of integrating semantic technologies into heritage management practices. I don't think that this omission is a reason to withold the paper for publication or seek further review. In fact I think it stands alone a paper quite well. Perhaps the authors might consider this as a complementary line of inquiry in their future work in the field.

In conclusion then, I believe the revised manuscript represents a valuable addition to the literature, pushing boundaries of how we assess, understand, and manage archaeological resources. Its focus on semantic technologies and the creation of spatial networks of meanings marks a significant step forward in the field. I believe its publication will stimulate further research and discussion, particularly in the realms of ethical and political considerations, which remain ripe for exploration. Therefore, I'm happy to endorse the publication of this manuscript.

Reference

[1] Alef, Y and Shafriri, Y. (2024). Cultural Significance Assessment of Archaeological Sites for Heritage Management: From Text of Spatial Networks of Meanings. Zenodo, 8309992, ver. 5 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8309992

Diamanti et al. (2024) is a significant contribution to the field of underwater robotics and their use in archaeology, with an innovative approach to some major problems in the deployment of said technologies. It identifies issues when it comes to approaching Underwater Cultural Heritage (UCH) sites and does so through an interest in the combination of data, maneuverability, and the interpretation provided by the instruments that archaeologists operate. The article's motives are clear: it is not enough to find the means to reach these sites, but rather is fundamental to take a step forward in methodology and how we can safeguard certain aspects of data recovery with robust mission planning.

To this end, the article does not fail to highlight previous contributions, in an intertwined web of references that demonstrate the marked evolution of the use of Unmanned Underwater Vehicles (UUVs), Remote Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs) and Autonomous Surface Vehicles (ASVs), which are growing exponentially in use (see Kapetanović et al. 2020). It should be emphasized that the notion of ‘aquatic environment’ used here is quite broad and is not limited to oceanic or maritime environments, which allows for a larger perspective on distinct technologies that proliferate in underwater archaeology. There is also a relevant discussion on the typologies of sensors and how these autonomous vehicles obtain their data, where are debated Inertial Measurement Units (IMU) and LiDAR systems. 

Thus, the authors of this article propose the creation of a model that acquires data through simulations, which allows for a better understanding of what a real mission presupposes in the field. Their tripartite method - pre-mission planning; mission plan and post-mission plan - offers a performing algorithm that simplifies and provides reliability to all the parts of the intervention. The use of real cases to create simulation models allows for a substantial approximation to common practice in underwater environments. And yet, the article is at its most innovative status when it combines all the elements it sets out to explore. It could simply focus on the methodological or planning component, on obtaining data, or on theoretical problems. But it goes further, which makes this approach more complete and of interest to the archaeological community. By not taking any part as isolated, the problems and possible solutions arising from the course of the mission are carried over from one parameter to another, where details are worked upon and efficiency goals are set.

One of the most significant cases is the tuning of ocean optics in aquatic environments according to the idiosyncracies of real cases (Diamanti et al. 2024: 8), a complex endeavor but absolutely necessary in order to increase the informative potential of the simulation. The exploration of various data capture models is also welcome, for the purposes of comparison and adaptation on a case-by-case basis. The brief theoretical reflection offered at the end of the article dwells in all these points and problematizes the difference between terrestrial and aquatic archaeology. In fact, the distinction does not only exist in the technical component, as although it draws in theoretical elements from archaeology that is carried out on land (see Krieger 2012 for this matter), the problems and interpretations are shaped by different factors and therefore become unique (Diamanti et al 2024: 15). The future, according to the authors, lies in increasing the autonomy of these vehicles so that the human element does not have to make decisions in a systematic way. It is in that note, and in order for that path to become closer to reality, that we strongly recommend this article for publication, in conjunction with the comments of the reviewers. We hope that its integrated approach, which brings together methods, theories and reflections, can become a broader modus operandi within the field of underwater robotics applied to archaeology.

References:

Diamanti, E., Yip, M., Stahl, A. and Ødegård, Ø. (2024). Advancing data quality of marine archaeological documentation using underwater robotics: from simulation environments to real-world scenarios, Zenodo, 8305098, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.8305098

Kapetanović, N., Vasilijević, A., Nađ, Đ., Zubčić, K., and Mišković, N. (2020). Marine Robots Mapping the Present and the Past: Unraveling the Secrets of the Deep. Remote Sensing, 12(23), 3902. MDPI AG. http://dx.doi.org/10.3390/rs12233902

Krieger, W. H. (2012). Theory, Locality, and Methodology in Archaeology: Just Add Water? HOPOS: The Journal of the International Society for the History of Philosophy of Science, 2(2), 243–257. https://doi.org/10.1086/666956