Cite this as: Jackson, S.E., Richissin, C.E., McCabe, E.E. and Lee, J.J. 2020 Data-Informed Tools for Archaeological Reflexivity: Examining the substance of bone through a meta-analysis of academic texts, Internet Archaeology 55. https://doi.org/10.11141/ia.55.12
Our study uses computational archaeology tools to investigate how researchers in our field present interpretations of the past in patterned ways. We do so in order to illuminate assumptions, naturalised categories, and patterned interpretative moves that may direct or impact the ways we interact with our evidence and write about our research. We approach this topic through a meta-analysis, using large-scale textual data from archaeological publications, focusing on the case study of bone. Are there patterned ways that archaeologists write about artefacts like bone that are visible when analysing larger datasets? If so, what underlying ideas shape these shared discursive moves? We present the results of three analyses: textual groundwork, conducted manually by field experts, and two machine-based interactive topic modelling visualisations (pyLDAvis and a hierarchical tree based on a Model of Models). Our results indicate that there are, indeed, patterns in our writing around how artefactual and archaeological materials are discussed, many of which are overt and sensical. However, our analyses also identify patterned discourses that are less obvious, but still part of regularised discourses in written narratives surrounding bone. These include: the use of multiple conceptual positions within, rather than simply between, articles, and a lack of patterned centrality of indigenous ontologies in how our field writes about bone. This pilot approach identifies data-informed, applied tools that will aid reflexive practices in our field. These operate at a scale that impacts future scholarly interactions with both evidence and published interpretations by shifting observation and reflection from an individual or small group exercise to a larger and more systematic process.
Corresponding author: Sarah E. Jackson 
sarah.jackson@uc.edu
Department of Anthropology, University of Cincinnati
Co-authors: Caleigh E. Richissin
Department of Anthropology, University of Cincinnati
Erin E. McCabe
Digital Scholarship Center, University of Cincinnati
James Jaehoon Lee
Digital Scholarship Center, University of Cincinnati
Figure 1: The top chart depicts the number of articles ('documents') over time that include the term 'bone'. The bottom chart shows the number of documents in relation to the number of times the search term 'bone' appears in those articles. 44.07% of these documents contain the search term 'bone' 5 or more times; 51.25% of documents mention bone 4+ times; 26.04% of documents mention bone 10+ times.
Figure 2: Complete view of the visualisation adapted from the Python programming package, pyLDAvis (Mabey 2015). Here, the highlighted Topic 2 of 25 is the second most prevalent topic with 6.6% of tokens. This topic contains terms pertaining to partibility such as skull, mandible, and portion. Terms like 'death' and 'trauma' indicate this topic's overlap with a more cultural consideration of bone.
Figure 3: Inset view of Figure 2. Topics 7, 8, and 16 are grouped together though relatively distant from other topics in the corpus. Together they represent 13.1% of the corpus tokens (5.2%, 5.1%, and 2.8% respectively). Terms contributing to the assessment of these topics are isotope, collagen, enamel, and carbon.
Figure 4: Inset view of Figure 2. Topic 1 is the most prominent topic representing 11.75% of corpus tokens. Topic 19 makes up a much smaller percentage of tokens in the corpus at only 2.1%. Topic 1 contains much broader archaeological language pertaining to burials while Topic 19 contains overlapping language, though this cluster's vocabulary is more culturally explicit, contributing to the difference in token percentage.
Figure 5: Complete view of the Multilevel Model of Models. This model visualisation displays hierarchical relationships between clusters and topics. This visualisation also allows for textual reengagement with documents as displayed in the document pane. This visualisation highlights clusters 0 and 2 as an example. These two clusters feature bone as a supporting role within discourses more central to other topics.
Figure 6: Inset view of Figure 5. Cluster 13 contains 16 subtopics making it one of the more broadly distributed cluster vocabularies, placing it in the top tier of prevalent clusters (third in terms of topic count). Examples of broad and general archaeological language within this cluster's vocabulary include structure, site, and archaeology.
Figure 7: Inset view of Figure 5. Cluster 4 contains only two subtopics making it one of the most narrowly distributed cluster vocabularies. The two subtopics within cluster 4 contain overlapping vocabulary suggesting a more tightly wound discourses related to burials. Contributing terms include burial, individual, and grave.
Figure 8: Inset view of Figure 5. Cluster 16 contains nine subtopics placing it in the middle range of cluster vocabularies within the corpus. Contributing terms to this cluster's vocabulary include general language such as site and archaeology, as well as more focused language such as isotope and diet.
Figure 9: Inset view of Figure 5. Highlighted Clusters 0 and 2 (viewed here with Cluster 1, not highlighted) contain terminology suggesting indigenous ideology and perspectives where bone plays a supporting role, rather than the primary focus of these topics.
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