The Levantine Corridor is considered a crossing point to Eurasia and one of the main areas for detecting population flows (and their associated cultural and economic changes) during the Pleistocene. This area could have been closed during the most arid periods, giving rise to processes of population isolation between Africa and Eurasia and intermittent contact between Eurasian human communities [1,2]. 

Zooarchaeological studies of the early Upper Palaeolithic assemblages constitute an important source of knowledge about human subsistence, making them central to the debate on modern behaviour. The Early Upper Palaeolithic sequence in the Levant includes two cultural entities – the Early Ahmarian and the Levantine Aurignacian. This latter is dated to 39-33 ka and is considered a local adaptation of the European Aurignacian techno-complex. In this work, the authors present a zooarchaeological study of the Nahal Rahaf 2 (ca. 35 ka) archaeological site in the southern Judean Desert in Israel [3].

Zooarchaeological data from the early Upper Paleolithic desert regions of the southern Levant are not common due to preservation problems of non-lithic finds. In the case of Nahal Rahaf 2, recent excavation seasons brought to light a stratigraphical sequence composed of very well-preserved archaeological surfaces attributed to the 'Arkov-Divshon' cultural entity, which is associated with the Levantine Aurignacian. 

This study shows age-specific caprine (Capra cf. Capra ibex) hunting on prime adults and a generalized procurement of gazelles (Gazella cf. Gazella gazella), which seem to have been selectively transported to the site and processed for within-bone nutrients. An interesting point to note is that the proportion of goats increases along the stratigraphic sequence, which suggests to the authors a specialization in the economy over time that is inversely related to the occupational intensity of use of the site. 

It is also noteworthy that the materials represent a large sample compared to previous studies from the Upper Paleolithic of the Judean Desert and Negev.

In summary, this manuscript contributes significantly to the study of both the palaeoenvironment and human subsistence strategies in the Upper Palaeolithic and provides another important reference point for evaluating human hunting adaptations in the arid regions of the southern Levant.

References

[1] Bermúdez de Castro, J.-L., Martinon-Torres, M. (2013). A new model for the evolution of the human pleistocene populations of Europe. Quaternary Int. 295, 102-112. https://doi.org/10.1016/j.quaint.2012.02.036

[2] Bar-Yosef, O., Belfer-Cohen, A. (2010). The Levantine Upper Palaeolithic and Epipalaeolithic. In Garcea, E.A.A. (Ed), South-Eastern Mediterranean Peoples Between 130,000 and 10,000 Years Ago. Oxbow Books, pp. 144-167.

[3] Marom, N., Gnezdilov, D. L., Shafir, R., Barzilai, O. and Shemer, M. (2022). Faunal remains from the Upper Paleolithic site of Nahal Rahaf 2 in the southern Judean Desert, Israel. BioRxiv, 2022.05.17.492258, ver. 4 peer-reviewed and recommended by Peer community in Archaeology. https://www.biorxiv.org/content/10.1101/2022.05.17.492258v4

Various biological systems have been subjected to mathematical modelling to enhance our understanding of the intricate interactions among different species. Among these models, the predator-prey model holds a significant position. Its relevance stems not only from its application in biology, where it largely governs the coexistence of diverse species in open ecosystems, but also from its utility in other domains. 

Predator-prey dynamics have long been a focal point in population ecology, yet access to real-world data is confined to relatively brief periods, typically less than a century. Studying predator-prey dynamics over extended periods presents challenges due to the limited availability of population data spanning more than a century. The most extensive dataset is the hare-lynx records from the Hudson Bay Company, documenting a century of fur trade [1]. However, other records are considerably shorter, usually spanning decades [2,3]. This constraint hampers our capacity to investigate predator-prey interactions over centennial or millennial scales. 

Marom and Wolkowski [4] propose here that leveraging regional radiocarbon databases offers a solution to this challenge, enabling the reconstruction of predator-prey population dynamics over extensive timeframes. To substantiate this proposition, they draw upon examples from Pleistocene Beringia and the Holocene Judean Desert. This approach is highly relevant and might provide insight into ecological processes occurring at a time scale beyond the limits of current ecological datasets. 

The methodological approach employed in this article proposes that the summed probability distribution (SPD) of predator radiocarbon dates, which reflects changes in population size, will demonstrate either more or less variation than anticipated from random sampling in a homogeneous distribution spanning the same timeframe. A deviation from randomness would imply a covariation between predator and prey populations. This basic hypothesis makes no assumptions about the frequency, mechanism, or cause of predator-prey interactions, as it is assumed that such aspects cannot be adequately tested with the available data. If validated, this hypothesis would offer initial support for the idea that long-term regional radiocarbon data contain signals of predator-prey interactions. This approach could justify the construction of larger datasets to facilitate a more comprehensive exploration of these signal structures.

References

[1] Elton, C. and Nicholson, M., 1942. The Ten-Year Cycle in Numbers of the Lynx in Canada. J. Anim. Ecol. 11, 215–244.

[2] Gilg, O., Sittler, B. and Hanski, I., 2009. Climate change and cyclic predator-prey population dynamics in the high Arctic. Glob. Chang. Biol. 15, 2634–2652. https://doi.org/10.1111/j.1365-2486.2009.01927.x

[3] Vucetich, J.A., Hebblewhite, M., Smith, D.W. and Peterson, R.O., 2011. Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems. J. Anim. Ecol. 80, 1236–1245. https://doi.org/10.1111/j.1365-2656.2011.01855.x

[4] Marom, N. and Wolkowski, U. (2024). A note on predator-prey dynamics in radiocarbon datasets, BioRxiv, 566733, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.1101/2023.11.12.566733

This paper (Stephan 2023) is about the use of video games as a pedagogical tool in class. Instead of taking the perspective of a lecturer, the author seeks the student’s perspectives to evaluate the success of an interactive teaching method at the crossroads of history, archaeology, and classics. The paper starts with a literature review, that highlights the intensive use of video games among college students and high schoolers as well as the impact video games can have on learning about the past. The case study this paper is based on is made with the game Assassin’s Creed: Odyssey, which is introduced in the next part of the paper as well as previous works on the same game. The author then explains his method, which entailed the tasks students had to complete for a class in classics. They could either choose to play a video game or more classically read some texts. After the tasks were done, students filled out a 14-question-survey to collect data about prior gaming experience, assignment enjoyment, and other questions specific to the assignments.

The results were based on only a fraction of the course participants (n=266) that completed the survey (n=26), which is a low number for doing statistical analysis. Besides some quantitative questions, students had also the possibility to freely give feedback on the assignments. Both survey types (quantitative answers and qualitative feedback) solely relied on the self-assessment of the students and one might wonder how representative a self-assessment is for evaluating learning outcomes. Both problems (size of the survey and actual achievements of learning outcomes) are getting discussed at the end of the paper, that rightly refers to its results as preliminary. I nevertheless think that this survey can help to better understand the role that video games can play in class. As the author rightly claims, this survey needs to be enhanced with a higher number of participants and a better way of determining the learning outcomes objectively. This paper can serve as a start into how we can determine the senseful use of video games in classrooms and what students think about doing so.

The paper (Callieri, M. et al. 2023) describes the “Dynamic Collections” project, an online platform initially created to showcase digital archaeological collections of Lund University. During a phase of testing by department members, new functionalities and artefacts were added resulting in an interactive platform adapted to university-level teaching and learning. The paper introduces into the topic and related works after which it starts to explain the project itself. The idea is to resemble the possibilities of interaction of non-digital collections in an online platform. Besides the objects themselves, the online platform offers annotations, measurement and other interactive tools based on the already known 3DHOP framework. With the possibility to create custom online collections a collaborative working/teaching environment can be created.

The already wide-spread use of the 3DHOP framework enabled the authors to develop some functionalities that could be used in the “Dynamic Collections” project. Also, current and future plans of the project are discussed and will include multiple 3D models for one object or permanent identifiers, which are both important additions to the system. The paper then continues to explain some of its further planned improvements, like comparisons and support for teaching, which will make the tool an important asset for future university-level education.

The paper in general is well-written and informative and introduces into the interactive tool, that is already available and working. It is very positive, that the authors rely on up-to-date methodologies in creating 3D online repositories and are in fact improving them by testing the tool in a teaching environment. They mention several times the alignment with upcoming EU efforts related to the European Collaborative Cloud for Cultural Heritage (ECCCH), which is anticipatory and far-sighted and adds to the longevity of the project. Comments of the reviewers were reasonably implemented and led to a clearer and more concise paper. I am very confident that this tool will find good use in heritage research and presentation as well as in university-level teaching and learning.

Although the authors never answer the introductory question explicitly (What characteristics should a virtual environment have in order to trigger dynamic interaction?), the paper gives the implicit answer by showing what the "Dynamic Collections" project has achieved and is able to achieve in the future.

Bibliography

Callieri, M., Berggren, Å., Dell'Unto, N., Derudas, P., Dininno, D., Ekengren, F., and Naponiello, G. (2023). The Dynamic Collections – a 3D Web Platform of Archaeological Artefacts designed for Data Reuse and Deep Interaction, Zenodo, 10067103, ver. 3 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.10067103

“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