The Emergence of Bone Technologies at the End of the Pleistocene in Southeast Asia: Regional and Evolutionary Implications
Bibliographic record
Abstract
For many decades Palaeolithic research viewed the development of early modern human behaviour as largely one of progress down a path towards the ‘modernity’ of the present. The European Palaeolithic sequence — the most extensively studied — was for a long time the yard-stick against which records from other regions were judged. Recent work undertaken in Africa and increasingly Asia, however, now suggests that the European evidence may tell a story that is more parochial and less universal than previously thought. While tracking developments at the large scale (the grand narrative) remains important, there is growing appreciation that to achieve a comprehensive understanding of human behavioural evolution requires an archaeologically regional perspective to balance this. One of the apparent markers of human modernity that has been sought in the global Palaeolithic record, prompted by finds in the European sequence, is innovation in bonebased technologies. As one step in the process of re-evaluating and contextualizing such innovations, in this article we explore the role of prehistoric bone technologies within the Southeast Asian sequence, where they have at least comparable antiquity to Europe and other parts of Asia. We observe a shift in the technological usage of bone — from a minor component to a medium of choice — during the second half of the Last Termination and into the Holocene. We suggest that this is consistent with it becoming a focus of the kinds of inventive behaviour demanded of foraging communities as they adapted to the far-reaching environmental and demographic changes that were reshaping this region at that time. This record represents one small element of a much wider, much longerterm adaptive process, which we would argue is not confined to the earliest instances of a particular technology or behaviour, but which forms part of an on-going story of our behavioural evolution.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".