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Enregistrement W3006502392 · doi:10.1002/evan.21819

Cultural taxonomies in the Paleolithic—Old questions, novel perspectives

2020· review· en· W3006502392 sur 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

Notice bibliographique

RevueEvolutionary Anthropology Issues News and Reviews · 2020
Typereview
Langueen
DomaineSocial Sciences
ThématiquePleistocene-Era Hominins and Archaeology
Établissements canadiensUniversité de Montréal
Organismes subventionnairesH2020 European Research CouncilAarhus Universitets ForskningsfondEuropean CommissionArts and Humanities Research CouncilAarhus Universitet
Mots-clésMesolithicPaleoanthropologyUpper PaleolithicArchaeologyMiddle PaleolithicPleistoceneHistoryGeographyConfusionAnthropologySociology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,944
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,006
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,091
Tête enseignante GPT0,416
Écart entre enseignants0,325 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2020
Routes d'admission1
Résumé présentoui

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