Journal of Archaeological Method and Theory最新文献

Observations about handaxe techno-morphology, like their symmetry, refinement, and fine edges have long been used to reconstruct the evolution of hominin cognition, skills, and technological decision making. However, these interpretations about the cognitive and technical abilities of Acheulean hominins often rely on the most ‘beautiful’ or supposedly ‘archetypical’ looking handaxes. But how often do these finely made handaxes actually occur in assemblages and how can we identify handaxes that were more skillfully made than others? Instead of seeking to estimate the skill level of individual past knappers, a trait that is oftentimes obscured in the archaeological record, we approach the question of knapping skill from the other direction. We instead ask how much skill was required to manufacture a handaxe? We explore, not the skill level of a handaxe’s maker, but how skillfully an individual handaxe was made. We put forth a suite of novel 3D methods of handaxe analysis for calculating their 3D edge sinuosity and 3D asymmetry. Using these methods, we quantify traits that are difficult to achieve during handaxe-making, providing an estimate of the requisite amount of ability, experience, attention, and effort demanded by their manufacture. Among our large sample of handaxes from the later Acheulean of the southern Levant, we find that blank size and tool-/site-use best explain the presence of more skillfully-made handaxes. Handaxes made on larger original blanks appear to afford more volume with which to enact longer and more skillfully demanding reduction sequences. Moreover, handaxes occurring at more recurrently occupied sites demanded less skill investment and appear geared towards the more expedient fulfilment of quotidian tasks. In the later Acheulean of the southern Levant there appears to be a great diversity in how skillfully handaxes were made, likely reflecting a diversity in the goals of handaxe making.

During my study of Senebtisi, an elite Egyptian woman from the Middle Kingdom period, I noted several missing skeletal elements which contradicted the archival and original representativeness of the individual. My initial interest into where, when, and how this loss occurred resulted in the following paper that sought to understand the ‘epi’-taphonomic factors influencing Senebtisi’s skeletal remains. Using object itinerary, the resultant narrative provides a cultural view to how these processes impact non-cultural factors, including skeletal preservation and the information available for study, all of which influence the bioarchaeological interpretative trajectory. Moments of movement and stillness for Senebtisi, including her representations (textual, photographs, drawings, and/or exhibits), afforded an understanding of alterations to her as an object and shifting value attributions since her exhumation in 1907. Ultimately, my focus on ‘epi’-taphonomic factors influencing Senebtisi exposes the subjectivity of her history as influenced by those that have studied her. Of note is the additive properties of processes that current (bio)archaeologists deem destructive, including reconstruction and unwrapping of mummified individuals. Importantly, my own study and research of Senebtisi represents additional interactions that influence the representations, representativeness of this individual, and preservation of her skeletal remains that ultimately result in further transformation to what is known and knowable about her.

From its earliest stages on, the rise of computational approaches in the humanities—whether in archaeology, history, or digital humanities more generally—has been accompanied by discussions and critical reflections on the way in which data-driven research methods are informed by the representation of research objects as data structures. Various dimensions, challenges, and characteristics can be roughly divided into three intersecting aspects: the subjectivity of data, their complexity, and their size. Archaeological network analysis as a formal, quantitative method is situated firmly within the tension between these fields, and many authors focus on the application of network research to archaeological data while respecting their complex nature. This paper adds to this growing body of work by focusing on the specificities of a medium-sized data set that offers multiple perspectives on a complex question of social archaeology: the study of intersecting social identities and their materialisation in funeral assemblages, particularly of a collective identity of high status-individuals or “elites”, during the Late Urnfield Period. It offers a mixed-methods approach that centres quantitative results and qualitative contextualization across different scales, and minimises loss of information and context, while transparently disclosing its practices of data selection, pre-processing, and analysis. In doing so, it aims to make the reflective positionings of “slow data” and “slow technology” productive for a methodology of “slow networks”.

Over the past decade, confocal microscopy has increasingly been employed to examine changes in stone tool surfaces and has proven to be an accurate technique for quantifying use-wear texture. Promising results have emerged from characterizing Polish formation on experimental and archaeological flint tools. Recent studies also highlighted the potential of confocal microscopy for analyzing tools made from reflective materials, such as quartzite. In this paper, we investigate the capability of confocal microscopy to discriminate use-wear on obsidian quantitatively. We examine whether confocal microscopy and 3D texture analysis can correctly classify several worked materials that are challenging to differentiate using the optical standard method of use-wear analysis. For cutting activities, we include butchery, de-skinning a fresh hide from grease and meaty tissues, cutting tanned leather, harvesting domestic ripe cereals, harvesting semi-green wild cereals, and sawing wet limestone. As for scraping activities, we explore discriminating differences among tools used for working dry hide, dry antler, soaked antler, fresh bone, softwood, fresh reeds, and wet limestone. Our results demonstrate that these worked materials can be confidently identified in experimental tools. While other relevant factors affecting use-wear texture, such as the intensity of use or post-depositional alterations, need to be controlled before employing the method on archaeological materials, our research suggests that the quantitative approach can enhance the standard method of use-wear analysis, providing unprecedented precision for identifying worked materials in obsidian tools.

The paper focuses on introducing 2D texture analysis as a quantitative method for functional analysis in archaeology. The paper aims to demonstrate the validity of this method for quantifying use-wear analysis and to evaluate different processing, extraction, and classification techniques. The method presented relies on five techniques of quantitative feature extraction from photographic images and nine classification techniques through machine learning algorithms. After creating a training dataset with experimental traces, machine learning models were validated through experimental and archaeological image classification. The best result achieved a classification accuracy of 80%, suggesting convolutional neural network and grey level co-occurence matrix as the best quantification options and neural networks as the best classification algorithm. The paper proposes to use the method as a fundamental tool in functional analysis to remove subjectivity criteria from traditional analysis and to address issues related to the credibility of the discipline, calibration, standardisation, and reproducibility of methods and results.

Ground stone tools are frequently found in archaeological contexts from early to late prehistoric times. These tools are key evidence for reconstructing past societies’ lifeways, technology and know-how, given their role in different tasks, including subsistence and craft activities. In recent years, the field of use-wear studies on ground stone tools showed an exponential growth in applying quantitative methods at all scales of observations, from macro to micro. These included using 3D models to measure and identify worked areas, spatial analyses for exploring trace and residue distribution, and confocal profilometers to analyse micro-polish texture. In this paper, we present results stemming from the application of confocal light microscopy and 3D surface texture analysis to identify and distinguish micro-polishes deriving from the processing of plant foods. We tested the potential of this method by analysing micro-polishes on modern limestone replicas used for grinding, pounding and dehusking wild and domestic cereal grains and legumes. Following a multi-level analysis, we demonstrate the efficacy of confocal microscopy and 3D surface texture analysis in correctly discriminating between different micro-polishes. We show how this method, integrated with the qualitative assessment of use-wear, may enhance functional interpretations of ground stone tools; we also discuss the current limits and future perspectives for their systematic application in the study of archaeological assemblages.

The process of plant domestication and subsequent adoption of agriculture have long been viewed by archaeologists as key factors in the emergence of social and political complexity. Ongoing research by botanists, archaeobotanists, and archaeologists, with ever-improving methodologies and technologies, reveal that the adoption of agriculture varies significantly in terms of chronologies, dietary intensity, and social impacts. It has become clear that broad-sweeping theories of agricultural adoption obscure meaningful micro-historical variations. Nowhere is this more true than in the Western Hemisphere, where the dates of the adoption of maize may differ in even geographically adjacent regions — thus the importance of focused regional studies of the history of maize consumption. In this review, we examine in detail the various methodological approaches employed in micro- and macro-botanical and isotopic studies and, importantly, appraise ongoing challenges to interpreting the findings of such research. We undertake this evaluation in the context of the northern midcontinent USA where these methodologies have produced regional maize histories that differ by as much as a thousand years in terms of both the presence of maize and the ultimate adoption of maize agriculture. We conclude that incorporating multiple refined methodological approaches is a key to understanding this variability.

Traditionally, reliable obsidian sourcing requires expensive calibration standards and extensive geological reference collections as well as experience with statistical processing. In the South Caucasus — one of the most obsidian-rich regions on the planet — this combination of requirements has often restricted sourcing studies because few projects have geological reference collections that cover all known obsidian sources. To test an alternative approach, we conducted “open sourcing” using portable X-ray fluorescence (pXRF) analyses of geological specimens with three key changes to the conventional method: (1) commercially available calibration standards were replaced with a loanable Peabody-Yale Reference Obsidians (PYRO) set, (2) a comprehensive geological reference collection was replaced with a published dataset of consensus values (Frahm, 2023a, 2023b), and (3) processing in statistical packages was replaced with two semiautomated machine-learning workflows available online. For comparison, we used classification by-eye with JMP 17.2 statistical software. Furthermore, we propose a new method to evaluate calibrations, which streamlines comparisons and which we refer to as a symmetric difference ratio (SDR). The results of this feasibility study demonstrate that this “open sourcing” workflow is reliable, yet currently only in combination with classification by-eye. When the consensus values were combined with the machine-learning solutions, the classification results were unsatisfactory. The most encouraging aspect of our alternative “open sourcing” workflow is that it enables correct source identification without physically measuring reference collections, therefore surmounting an obstacle that, until now, has severely limited archaeological research. We anticipate that rapid developments in machine-learning will also soon improve the workflow.

By developing a new methodology for handling and assessing a large number of shoreline dated sites, this paper compares the summed probability distribution of radiocarbon dates and shoreline dates along the Skagerrak coast of south-eastern Norway. Both measures have previously been compared to elucidate demographic developments in Fennoscandia, but these have not been based on probabilistic methods for shoreline dating. The findings indicate a largely diverging development of the two data sets through the Mesolithic. The number of shoreline dated sites undergoes some process of overall decrease through the period, while the radiocarbon data is characterised by a lacking signal in the earliest parts of the period and then undergoes a logistic growth that quickly plateaus and remains stable for the remainder of the period. The precise nature of this discrepancy will necessitate further substantiation and the method of shoreline dating itself still requires further testing and assessment. Having noted this, we still tentatively suggest that while the number of shoreline dated sites is not devoid of influence from a demographic signal, this measure could be more heavily influenced by mobility patterns. Conversely, we also suggest that the lacking radiocarbon data from the earliest part of the Mesolithic is in part the result of mobility patterns, but that the radiocarbon data could be reflecting population dynamics more directly.

Recent advances in archaeogenomics have granted access to previously unavailable biological information with the potential to further our understanding of past social dynamics at a range of scales. However, to properly integrate these data within archaeological narratives, new methodological and theoretical tools are required. Effort must be put into finding new methods for weaving together different datasets where material culture and archaeogenomic data are both constitutive elements. This is true on a small scale, when we study relationships at the individual level, and at a larger scale when we deal with social and population dynamics. Specifically, in the study of kinship systems, it is essential to contextualize and make sense of biological relatedness through social relations, which, in archaeology, is achieved by using material culture as a proxy. In this paper, we propose a Network Science framework to integrate archaeogenomic data and material culture at an intra-site scale to study biological relatedness and social organization at the Neolithic site of Çatalhöyük. Methodologically, we propose the use of network variance to investigate the association between biological relatedness and material culture within networks of houses. This approach allows us to observe how material culture similarity between buildings is associated with biological relationships between individuals and how biogenetic ties concentrate at specific localities on site.