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Record W3006502392 · doi:10.1002/evan.21819

Cultural taxonomies in the Paleolithic—Old questions, novel perspectives

2020· review· en· W3006502392 on OpenAlexaff
Felix Riede, Astolfo Gomes de Mello Araújo, C. Michael Barton, Knut Andreas Bergsvik, Huw S. Groucutt, Shumon T. Hussain, Javier Fernández‐López de Pablo, Andreas Maier, Ben Marwick, Lydia Pyne, Kathryn L. Ranhorn, Natasha Reynolds, Julien Riel‐Salvatore, Florian Sauer, Kamil Serwatka, Annabell Zander

Bibliographic record

VenueEvolutionary Anthropology Issues News and Reviews · 2020
Typereview
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversité de Montréal
FundersH2020 European Research CouncilAarhus Universitets ForskningsfondEuropean CommissionArts and Humanities Research CouncilAarhus Universitet
KeywordsMesolithicPaleoanthropologyUpper PaleolithicArchaeologyMiddle PaleolithicPleistoceneHistoryGeographyConfusionAnthropologySociology

Abstract

fetched live from OpenAlex

Arguably, these four requirements are essential for conducting comparative and cumulative research at a supra-regional and diachronic scale, and for articulating sequences of culture change in the Paleolithic with paleogenomic, paleoecological or paleoclimatic data. Most commonly, different forms of the typological method have been used to construct such archeological cultures. Taxonomic issues are by no means restricted to the Paleolithic but take on a specific quality there as our temporal scales stretch from the near-paleontological of the Middle Pleistocene to the more intuitively appreciable timescales of the Final Paleolithic. The recurring debates about Paleolithic systematics together with recent research in many parts of the world and across many of its subperiods—from the Early Stone Age to the Epipaleolithic—have shown, however, that a substantial number of traditional archeological types are no longer doing their diagnostic work and that many formally named archeological units based on such types contribute more to confusion rather than solution in regard to our core questions.7-11 These issues are at the core of the European Research Foundation-funded project entitled CLIOdynamic ARCHaeology: Computational approaches to Final Paleolithic/earliest Mesolithic archaeology and climate change (CLIOARCH: http://cas.au.dk/en/ERC-clioarch/) and the workshop on which we report here sought to catalyze joint thinking on Paleolithic systematics in a diachronic and global perspective. On November 27–29, 2019, the CLIOARCH project organized a workshop titled “All these fantastic cultures? Cultural taxonomies in the Paleolithic—old questions, novel perspectives” at Sandbjerg Manor in Southern Denmark. The conference venue is owned by Aarhus University and allows small groups of researchers to come together without quotidian interruptions to focus in on particular concerns. The meeting was funded jointly by the European Research Council via CLIOARCH and the Aarhus University Research Foundation. Sixteen participants from 10 different countries—reporting on work conducted in a much larger number of countries (Figure 1)—came together over a 3-day period. The composition of participants was carefully designed to bring together workers who would rarely, if ever, meet at their regular conferences and who could, collectively, address the widespread and diachronic nature of the issues at hand. Grounded in reviews of research history, the epistemologies and practice of Paleolithic classification and taxonomy were discussed. Together, we examined how such practices differed between different research traditions and regions (e.g., North American, South American, French, Eastern European), across spatiotemporal scales of analysis from multimillennial to centennial and from continental to microregional, and in relation to a bewildering array of well-known and more obscure “cultures”: the Nubian Complex; the Nasera and Mumba Industries; the Uluzzian and Protoaurignacian; the Sonvian; the Gravettian, Spitsynian, Aurignacian, Streletskian, Gorodtsovian; the Magdalenian and Final Upper Magdalenian; the Azilian, Azurian and Epipaleolithic, the Epimagdalenian and Sauveterrian; the Itaparica, Lagoa Santa, and Umbu Traditions; the Swiderian, the Federmesser groups and all its fantastic subgroups; the Long Blade Industry, Epi-Ahrensburgian, Belloisian, and Laborian; the Dwelling site culture, the Slate culture, as well as the Funnel beaker culture. Two days of presentations were followed by half-a-day of discussion, which drew out both agreements and disagreements. At the end of the meeting, most of us were more hopeful with regard to Paleolithic cultural taxonomies than ever before (Figure 2). Robust classification and cultural taxonomy, we all agreed, are essential for creating analytical units that stand the test of epistemological scrutiny. While published almost half a century ago, the landmark book Systematics in Prehistory13 was mentioned frequently during the workshop. While we distance ourselves from the author, we do note that this book not only laid out a clear-sighted protocol for object classification, it also laid the foundation for later evolutionary approaches that have since matured into a most productive intellectual endeavor (recently summarized in Prentiss.14 In line with these evolutionary perspectives, the workshop concluded also with emphasizing the need to link notions of cultural transmission to classification, making them theory-driven and epistemologically defensible. By the same token, we all agreed that quantitative methods offer the most transparent and robust means of integrating the vast number of observations made at the level of the artifact into nested, higher-order taxonomies that group artifacts into assemblages, assemblages into clusters, and so on. Multivariate statistics and in particular network and clustering algorithms were identified as particularly useful tools for visualizing the hypothesized relations between our operational units. It is here where the history of archeology, as became evident throughout the workshop, also intersects in salient ways with the history of computation. While early researchers such as Robert Dunnell or David Clarke15 proposed useful conceptual tools, they were strongly constrained in their application by the limited availability of computers and the then only nascent data handling tools available. In biological taxonomy, the introduction of computers is well known to have not only invigorated but also revolutionized the field16—and the same we argue is set to happen in Paleolithic archeology. At what spatial and temporal scale and on the basis of which material matters of cultural taxonomy are best resolved and precisely which methods constitute an analytical gold standard remains to be resolved. Nonetheless, when an epistemological and computational invigoration is coupled to the more widespread adoption of Open Science and Team Science principles,17 we may be able to rapidly move on from creating more and more mutually incompatible cultural taxonomies to the arguably more exciting business of using our taxonomies to understand the past patterns and processes of convergent and divergent cultural evolution, resilience, migration, and adaptation.18, 19 Epistemologically robust, empirically grounded, and operational taxonomies are the building blocks of good Paleolithic archeology. If the goals of constructing such taxonomies can be achieved, we concluded, practitioners can engage more confidently in interdisciplinary collaborations with other paleoscientists and we may also be able to accelerate the pace of cumulative analytical discoveries. The workshop reported here was sponsored primarily by the European Research Council (ERC) project CLIOARCH, under the European Union's Horizon 2020 research and innovation program (grant agreement No. 817564). In addition, the support of the Aarhus University Research Foundation (#AUFF-E-2019-FLS-1-25) and the warm welcome by the Sandbjerg Manor staff are gratefully acknowledged. The authors declare no potential conflict of interest. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.416
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations23
Published2020
Admission routes1
Has abstractyes

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