Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".