In a recent article, Fumihiro Sakahira and Hiro'omi Tsumura (2023) used social network analysis methods to analyze change in obsidian trade networks in Japan throughout the 13,000-year-long Jomon period. In the paper recommended here (Sakahira and Tsumura 2024), Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction they revisit that data and describe additional analyses that confirm the robustness of their social network analysis. The data, analysis methods, and substantive conclusions of the two papers overlap; what this new paper adds is a detailed examination of the data and methods, including use of bootstrap analysis to demonstrate the reasonableness of the methods they used to group sites into clusters.

Both papers begin with a large dataset of approximately 21,000 artifacts from more than 250 sites dating to various times throughout the Jomon period. The number of sites and artifacts, varying sample sizes from the sites, as well as the length of the Jomon period, make interpretation of the data challenging. To help make the data easier to interpret and reduce problems with small sample sizes from some sites, the authors assign each site to one of five sub-periods, then define spatial clusters of sites within each period using the DBSCAN algorithm. Sites with at least three other sites within 10 km are joined into clusters, while sites that lack enough close neighbors are left as isolates. Clusters or isolated sites with sample sizes smaller than 30 were dropped, and the remaining sites and clusters became the nodes in the networks formed for each period, using cosine similarities of obsidian assemblages to define the strength of ties between clusters and sites.

The main substantive result of Sakahira and Tsumura’s analysis is the demonstration that, during the Middle Jomon period (5500-4500 cal BP), clusters and isolated sites were much more connected than before or after that period. This is largely due to extensive distribution of obsidian from the Kozu-shima source, located on a small island off the Japanese mainland. Before the Middle Jomon period, Kozu-shima obsidian was mostly found at sites near the coast, but during the Middle Jomon, a trade network developed that took Kozu-shima obsidian far inland. This ended after the Middle Jomon period, and obsidian networks were less densely connected in the late and last Jomon periods.

The methods and conclusions are all previously published (Sakahira and Tsumura 2023). What Sakahira and Tsumura add in Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction are:

·       an examination of the distribution of cosine similarities between their clusters for each period

·       a similar evaluation of the cosine similarities within each cluster (and among the unclustered sites) for each period

·       bootstrap analyses of the mean cosine similarities and network densities for each time period

These additional analyses demonstrate that the methods used to cluster sites are reasonable, and that the use of spatially defined clusters as nodes (rather than the individual sites within the clusters) works well as a way of reducing bias from small, unrepresentative samples. An alternative way to reduce that bias would be to simply drop small assemblages, but that would mean ignoring data that could usefully contribute to the analysis.

The cosine similarities between clusters show patterns that make sense given the results of the network analysis. The Middle Jomon period has, on average, the highest cosine similarities between clusters, and most cluster pairs have high cosine similarities, consistent with the densely connected, spatially expansive network from that time period. A few cluster pairs in the Middle Jomon have low similarities, apparently representing comparisons including one of the few nodes on the margins on the network that had little or no obsidian from the Kozu-shima source. The other four time periods all show lower average inter-cluster similarities and many cluster pairs have either high or low similarities. This probably reflects the tendency for nearby clusters to have very similar obsidian assemblages to each other and for geographically distant clusters to have dissimilar obsidian assemblages. The pattern is consistent with the less densely connected networks and regionalization shown in the network graphs. Thinking about this pattern makes me want to see a plot of the geographic distances between the clusters against the cosine similarities. There must be a very strong correlation, but it would be interesting to know whether there are any cluster pairs with similarities that deviate markedly from what would be predicted by their geographic separation.

The similarities within clusters are also interesting. For each time period, almost every cluster has a higher average (mean and median) within-cluster similarity than the similarity for unclustered sites, with only two exceptions. This is partial validation of the method used for creating the spatial clusters; sites within the clusters are at least more similar to each other than unclustered sites are, suggesting that grouping them this way was reasonable.

Although Sakahira and Tsumura say little about it, most clusters show quite a wide range of similarities between the site pairs they contain; average within-cluster similarities are relatively high, but many pairs of sites in most clusters appear to have low similarities (the individual values are not reported, but the pattern is clear in boxplots for the first four periods). There may be value in further exploring the occurrence of low site-to-site similarities within clusters. How often are they caused by small sample sizes? Clusters are retained in the analysis if they have a total of at least 30 artifacts, but clusters may contain sites with even smaller sample sizes, and small samples likely account for many of the low similarity values between sites in the same cluster. But is distance between sites in a cluster also a factor? If the most distant sites within a spatially extensive cluster are dissimilar, subdividing the cluster would likely improve the results. Further exploration of these within-cluster site-to-site similarity values might be worth doing, perhaps by plotting the similarities against the size of the smallest sample included in the comparison, as well as by plotting the cosine similarity against the distance between sites. Any low similarity values not attributable to small sample sizes or geographic distance would surely be worth investigating further.

Sakahira and Tsumura also use a bootstrap analysis to simulate, for each time period, mean cosine similarities between clusters and between site pairs without clustering. They also simulate the network density for each time period before and after clustering. These analyses show that, almost always, mean simulated cosine similarities and mean simulated network density are higher after clustering than before. The simulated mean values also match the actual mean values better after clustering than before. This improved match to actual values when the sites are clustered for the bootstrap reinforces the argument that clustering the sites for the network analysis was a reasonable result.

The strength of this paper is that Sakahira and Tsumura return to reevaluate their previously published work, which demonstrated strong patterns through time in the nature and extent of Jomon obsidian trade networks. In the current paper they present further analyses demonstrating that several of their methodological decisions were reasonable and their results are robust. The specific clusters formed with the DBSCAN algorithm may or may not be optimal (which would be unreasonable to expect), but the authors present analyses showing that using spatial clusters does improve their network analysis. Clustering reduces problems with small sample sizes from individual sites and simplifies the network graphs by reducing the number of nodes, which makes the results easier to interpret.

Reference

Sakahira, F. and Tsumura, H. (2023). Tipping Points of Ancient Japanese Jomon Trade Networks from Social Network Analyses of Obsidian Artifacts. Frontiers in Physics 10:1015870. https://doi.org/10.3389/fphy.2022.1015870

Sakahira, F. and Tsumura, H. (2024). Social Network Analysis of Ancient Japanese Obsidian Artifacts Reflecting Sampling Bias Reduction, Zenodo, 10057602, ver. 7 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7969330

Bischoff (2023) is a significant contribution to the growing field of geometric morphometric analysis in stone tool analysis. The subject is projectile points from the southwestern United States. Projectile point typologies or systematics remain an important part of North American archaeology, and in fact these typologies continue to be used primarily as cultural-historical markers. This article looks at projectile point types using a 2D image geometric morphometric analysis as a way of both improving on projectile point types but also testing if these types are in fact based in measurable reality. A total of 164 point outlines are analyzed using Elliptical Fourier, semilandmark and landmark analyses. The author also uses a network analysis to look at possible relationships between projectile point morphologies in space. This is a clever way of working around the predefined distributions of projectile point types, some of which are over 100 years old. Because of the dynamic nature of stone tools in terms of their use, reworking and reuse, this article can also provide solutions for studying the dynamic nature of stone tools. This article therefore also has a wide applicability to other stone tool analyses.

Reference

Bischoff, R. J. (2023) Geometric Morphometric Analysis of Projectile Points from the Southwest United States, SocArXiv, a6wjc, ver. 8 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.31235/osf.io/a6wjc

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

Archaeological data is inherently uncertain, which is probably why Bayesian approaches have become increasingly valued within the discipline. For radiocarbon analysis, instead of pinpointing one "true" date, Bayesian methods embrace probability, telling us how likely a sample falls within different date ranges based on what we already know (the prior) and what our data tells us (the posterior).

In this new paper, Miguel de Navascués and colleagues offer a method for treating radiocarbon dates as count data (generated through a Poisson process), rather than viewing them as draws from a probability distribution. This shift allows them to model the expected number of samples per year and incorporate uncertainty in both the timing and total number of samples. The result is a more natural representation of how radiocarbon samples accumulate in the archaeological record over time. (They then demonstrate the method using data from Britain and Ireland, revealing patterns that both confirm and refine our understanding of population changes during key transitions, including a possibly earlier start to the Neolithic demographic expansion.)

Overall, the paper represents a valuable contribution to quantitative archaeology that complements, rather than replaces, existing approaches like Sum Probability Distributions (SPDs) and end-to-end Bayesian methods (e.g., see Crema 2022 and Price et al. 2021).

While mathematically heavy, the paper is accompanied by well-annotated R scripts that I encourage readers to experiment with. For researchers working with radiocarbon data, particularly those investigating demographic change or cultural transmission, the methods presented here offer important new analytical possibilities for understanding past human dynamics. Sometimes, to move forward, we just need to count differently.

References

Crema, E. R. (2022), Statistical Inference of Prehistoric Demography from Frequency Distributions of Radiocarbon Dates: A Review and a Guide for the Perplexed. Journal of Archaeological Method and Theory 29(4):1387–1418. https://doi.org/10.1007/s10816-022-09559-5

de Navascués, M., Burgarella, C. & Jakobsson, M. (2025) Analysis of the Abundance of Radiocarbon Samples as Count Data. Zenodo, ver.3 peer-reviewed and recommended by PCI Archaeology https://doi.org/10.5281/zenodo.13381596

Price, M. H., Capriles, J. M., Hoggarth, J. A., Bocinsky, R. K., Ebert, C. E. & Jones, J. H. (2021) End-to-End Bayesian Analysis for Summarizing Sets of Radiocarbon Dates. Journal of Archaeological Science 135:105473. https://doi.org/10.1016/j.jas.2021.105473

“The density of types and the dignity of the fragment. A website approach to archaeological typology” by G. Buccellati and M. Kelly-Buccellati (1) is a contribution to the rapidly growing literature on digital approaches to archaeological data management, expertly showcasing the significant theoretical and epistemological impetus of such work. The authors offer a conceptually lucid discussion of key concepts in archaeological ordering practices surrounding the longstanding tension between so-called ‘etic’ and ‘emic’ approaches, thereby providing a thorough systematic of how to think through sameness and difference in the context of voluminous digital archaeological data. 

As a point of departure, the authors reconsider the relationship between archaeological fragments – spatiotemporally bounded artefacts and features – and their larger meaning-giving totality as the primary locus of archaeological knowledge. Typology can then be said to serve this overriding quest to resolve the conflict between parts and wholes, as the parts themselves are never sufficient to render the whole but the whole remains elusive without reference to the parts. Buccellati and Kelly-Buccellati here make an interesting point about the importance to register the globality of the archaeological record – that is, literally everything encountered in the soil – without making any prior choices as to what supposedly matters and what not. The distinctiveness of the archaeological enterprise, according to them, indeed consists of the circumstance that merely disconnected fragments come to the attention of archaeologists and the only objective data that can be attained, because of this, are about the situated location of fragments in the ground and their relation to other fragments – what they call ‘emplacement’. This, we would add, includes the relationship of fragments with human observers and the employed methods of excavation as observation. As the authors say: “[i]t is in this sense that the fragments are natively digital: they are atoms that do not cohere into a typological whole”.

The systematic exploration of how the so recovered fragments may be re-articulated is then essentially the goal of archaeological categorization and typology but these practices can only ever be successful if the whole context of original ‘emplacement’ is carefully taken into consideration. This reconstruction of the fundamental epistemological situation archaeology finds itself in leads the authors to a general rejection of ‘more’ vs. ‘less’ objective or even subjective ordering practices as such qualifications tend to miss the point. What matters is to enable the flexible and scalable confrontation of isolated archaeological fragments, to do experiment with and test different part-whole relations and their possible knowledge contributions. It is no coincidence that the authors insist on a dynamical approach to ordering practices and type-thinking in archaeology here, which in many ways comes often very close to the general conceptual orientation philosopher Stephen C. Pepper (2) has called ‘organicism’ – a preoccupation of resolving the tension between heterogeneous fragments and coherent wholes without losing sight of the specificity of each single fragment. In the view of organicist thinkers, and the authors seem to share this recognition, to take complexity seriously means to centre the dialectics between fragments and wholes in their entirety. This notion is directly reflected in the authors’ interesting definition of ‘big data’ in archaeology as a multi-layered and multi-referential system of organizing the totality of observations of emplacement (the Global record).

Based on this broader exposition, Buccellati and Kelly-Buccellati make some perceptive and noteworthy observations vis-à-vis the aforementioned emic-etic distinction that has caused so much archaeological confusion and debate (3–6). To begin with, emic and etic designate different systemic logics of organizing observable sameness and difference. Emic systems are closed and foreground the idea of the roster, they recognize only a limited set of types whose identity depends on relative differences. Etic systems, on the other hand, are in principle open (and even open-ended) and rely on the notion of the lexicon; they enlist a principally endless repertoire of traits, types and sub-types (classes and sub-classes may be added to this list of course). Difference in etic systems is moreover defined according to some general standards that appear to eclipse the standards of the system itself. Etic systems therefore tend to advocate supposedly universal principles of how to establish similarity vs. difference, although, in reality, there is substantial debate as to what these principles may be or whether such endeavour is a useful undertaking. In the wild, both etic and emic systems of ordering and categorization are of course encountered in the plural but etic systems deploy external standards of order while emic systems operate via internal standards. An interesting observation by the authors in this context is that archaeological reasoning in relation to sameness and difference is almost never either exclusively etic or exclusively emic. The simple reason is that any grouping of fragments according to technological (means/modes of production) or functional considerations (use-wear, tool design, relation between form and function) based on empirical evidence is typically already infused by emic standards. The classic example from the analysis of archaeological pottery is ware groups, which reference the nexus of technological know-how and concrete practices, and which rely, in a given context, on internal, relative differentiations between the respective observed practices. Yet ignoring these distinctions would sideline significant knowledge on the past.

These discussions are refreshing as they may indicate that ordering practices – when considered as an end in themselves – misconstrue the archaeological process as static and so advocate for categories, classes, and types to be carved out before any serious analysis can begin. It could in fact be argued that in doing so, they merely construct a new closed system, then emic by definition. Buccellati and Kelly-Buccellati propose an alternative without discarding the intuition that ordering archaeological materials is conditional to the inferential and knowledge-production process: they propose that typologies should be treated as arguments. Moreover, the sort of argument they have in mind is to a lesser extent ‘formal-logical’ but instead emphatically ‘dialogical’ in nature, as such argumentative form helps to combat the inherent static-ness of ordering practices the authors criticize, and so discloses a radically dynamic approach to the undertaking of fragment-whole matching. The organicist inclination to preserve ‘the dignity of fragments’ while working towards their resolution in attendant wholes and sub-wholes further gives rise to the idea that such ‘native digital fragments’ must be brought into systematic conversation with one another, acknowledging the involved complexity. To this end, the authors frame ordering work and typo-praxis as a ‘digital discourse’ and ask what the conditions and possibilities for such discourse are and how it can be facilitated. It is here that they put forward the idea that the webpage may provide an ideal epistemic model system to promote the preservation of emplaced archaeological fragments while simultaneously promoting multistranded and multi-context explorations of fragment coherence and articulation. The website enables unique forms of exploration and engagement with data and new arguments escaping the fixity of the analogue-printed which dominates current archaeological practice. Similar experiences were for example made in the context of Gardin’s ‘logicism’, leading to broadly comparable attempts to overcome the analogue with more dynamic, HTML/web-based forms of data presentation, exploration and discussion (7, 8).

As such, Buccellati and Kelly-Buccellati table a range of fresh arguments for re-thinking typology beyond and with text at the same time, to enable ‘dynamic reading’ of fragment-whole relationships in an increasingly digital world. Their proposal comes thereby close to what has been termed ‘deep mapping’ in the context of critical cartographies and other spatially-inclined scholarship in the Anglophone world (9, 10). Deep maps seek to transcend the epistemological limitations of 2D-representations of spatiality on traditional maps and introduce different layers of informational depth and heterogeneity, which, similarly to the living digital webpage proposed by the authors, can be continuously extended and revised and which may also greatly promote multidisciplinary and team-based research endeavours. In the same spirit as the authors’ ‘digital discourse’, deep mapping draws attention to the knowledge potential of bringing together the heterogeneous, the etic and the emic, and to pay more attention to ‘multiplanar’ and ‘multilinear’ relationships as well as the associated relations of relations. This proposal to deploy types and typology in general as dynamic arguments is linked to the ambition to contribute to and work on the narrativization of the archaeological record without tacit (and often unconscious) conceptual pre-subscription, countering typologies that remain largely in the abstract and so have contributed to the creeping anonymity of the past.

Bibliography

1. Buccellati, G. and Kelly-Buccellati, M. (2023). The Density of Types and the Dignity of the Fragment. A website approach to archaeological typology, Zenodo, 7743834, ver. 4 peer-reviewed and recommended by Peer Community in Archaeology. https://doi.org/10.5281/zenodo.7743834.

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

3. Hayden, B. (1984). Are Emic Types Relevant to Archaeology? Ethnohistory 31, 79–92. https://doi.org/10.2307/482057

4. Tostevin, G. B. (2011). An Introduction to the Special Issue: Reduction Sequence, Chaîne Opératoire, and Other Methods: The Epistemologies of Different Approaches to Lithic Analysis. PaleoAnthropology, 293−296. https://www.doi.org/10.4207/PA.2011.ART59

5. Tostevin, G. B. (2013). Seeing lithics: a middle-range theory for testing for cultural transmission in the pleistocene (Oakville, CT: Oxbow Books).

6. Boissinot, P. (2015). Qu’est-ce qu’un fait archéologique? (Éditions EHESS). https://doi.org/10.4000/lectures.19921

7. Gardin, J.-C. and Roux, V. (2004). The Arkeotek Project: a European Network of Knowledge Bases in the Archaeology of Techniques. Archeologia e Calcolatori 15, 25–40.

8. Husi, P. (2022). La céramique médiévale et moderne du bassin de la Loire moyenne, chrono-typologie et transformation des aires culturelles dans la longue durée (6e—17e s.) (FERACF).

9. Bodenhamer, D. J., Corrigan, J. and Harris, T. M. (2015). Deep Maps and Spatial Narratives (Indiana University Press).

10. Gillings, M., Hacigüzeller, P. and Lock, G. R. (2019). Re-mapping archaeology: critical perspectives, alternative mappings (Routledge).