Gender Prehistory: Shaping Techniques applied to Osseous Artefacts
Bibliographic record
Abstract
Scraping and abrasion are quite ancient and universal techniques. On bone, antler, and teeth, these two techniques can sometimes be used for the reduction sequence but mainly serve for shaping, even though chronological, cultural, and geographical gradients can be observed. In some archaeological assemblages, during the European Neolithic for example, abrasion dominates whatever the type of object manufactured. In contrast, in the Arctic Pre-Inuit and Inuit contexts scraping largely predominates. Our goal is to question the variations from an almost complete exclusivity for only one technical practice to a mix of both. In particular, could the sexual division of labour influence both the technical and social spheres of activity? Why for a given type of artifact is a clear choice sometimes made for only one technique when both scraping and abrading are encountered within a single assemblage? To address these questions, we compared data obtained from different socio-economic and environmental contexts. The techniques used to produce osseous and lithic tools by Neolithic and Epipaleolithic groups from Europe, Near East and Maghreb have been analyzed and compared to those encountered in the American Arctic societies. Ethnographical comparisons help in analyzing potential links between techniques, lifestyles and gender. High-mobility, hunter-gatherer subsistence and scraping seem to be in association and opposed to sedentary, farming, milling, women activity and abrasion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".