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Enregistrement W4297920378 · doi:10.31219/osf.io/nf27g

Sourcing Oldowan and Acheulean Stone Tools in Eastern Africa

2022· preprint· en· W4297920378 sur OpenAlexaff
Julien Favreau

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineSocial Sciences
ThématiquePleistocene-Era Hominins and Archaeology
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésAcheuleanOlduvai GorgeGeologyPaleoanthropologyTaphonomyPlio-PleistocenePleistoceneGeographyPrecambrianPaleontologyArchaeologyEarth science

Résumé

récupéré en direct d'OpenAlex

Eastern Africa’s Plio-Pleistocene sedimentary record has shaped our understanding of human evolution and the development of stone tool technologies. Over the years, raw material sourcing has emerged as a key research topic in lithic analysis as it can allow for the identification of resource extraction points together with anthropogenic transport distances across palaeolandscapes as a means to infer other aspects of hominin behaviour. The goal of this article is to review and synthesise the aims, methods, challenges, and current knowledge on the sourcing of Oldowan and Acheulean stone tools from Plio-Pleistocene archaeological contexts in Eastern Africa, namely Ethiopia, Kenya, and Tanzania. Beginning with the theoretical framework of lithic sourcing, a systematic, comprehensive, and multifactorial methodology is presented, which is applicable across archaeology’s sub-disciplinary boundaries. From thereon, the geology of Eastern Africa is reviewed to establish the range of available rock types during the Plio-Pleistocene along with the processes that led to the formation of primary and secondary raw material sources. Several drivers are identified to have affected the distribution, availability, and prehistoric utilisation of raw materials across Eastern Africa, including Precambrian tectono-thermal events, planation, and subsequent uplift, followed by Cenozoic volcanism, extensional faulting, subsidence, sedimentation, and geomorphological forcing. The following section comprises of a comprehensive review of Oldowan and Acheulean raw material provisioning in Eastern Africa to identify the state of knowledge and methodological trends. Four important patterns are identified: (1) Oldowan and Acheulean toolmakers regularly exerted selective criteria when choosing raw materials; (2) the spatio-temporal fragmentation of technological activities across the palaeolandscape emerged before the first appearance datum of the Acheulean; (3) hominin toolmakers preferentially utilised igneous rock types followed by metamorphic and sedimentary lithologies mirroring Eastern Africa’s lithostratigraphic sequence; and (4) Acheulean toolmakers largely mimicked their Oldowan counterparts in terms of raw material provisioning until the late Early Pleistocene, when they began to engage in qualitatively different behaviour best evidenced by anthropogenic stone transport over increasingly longer distances. Another informative pattern that emerges from the foregoing section is the limited efforts that have been historically devoted in sourcing lithic raw materials of Plio-Pleistocene age. This can be attributed to several factors, not least of which are a suite of interpretive challenges such as time-averaging, recycling, sourcing, and identifying selection criteria, which are featured in the ensuing section. The foregoing discussion also serves to preface a devoted section on the array of analytical methods that can be successfully employed by archaeologists to source lithic raw materials. Proven and innovative analytical methods are identified by bringing into dialogue key factors such as accuracy, precision, reproducibility, discriminatory power, sensitivity, destructiveness, throughput, cost, ease, and the question of spatial scale. It is also found that characterising non-obsidian lithologies is best accomplished using more than one analytical method with the understanding that once positive baseline results are obtained, subsequent archaeological testing can be narrowed down methodologically. Regardless of the analytical method of choice, it is imperative to implement effective means to analyse the resulting data, which constitutes the main topic addressed in the following section supplemented by two case studies. It is found that variably sophisticated forms of multivariate statistics are usually required to discriminate non-obsidian sources to probabilistically source stone tools, and it is argued that reporting is best done following the principles of open science. The final section reviews ethnographic and archaeological literature on sourcing, mobility, and geospatial modelling to outline their potential to enhance the interpretive remit of traditional raw material studies. Ultimately, this article reviews the state of knowledge about raw material sourcing in the Earlier Stone Age across Eastern Africa and highlights means through which archaeologists can garner the full potential of a non-renewable record.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,004
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,073
Tête enseignante GPT0,325
Écart entre enseignants0,252 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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