Raw material economics in their environmental context: an example from the Middle Palaeolithic of southern France
Bibliographic record
Abstract
Abstract To understand the human behaviour reflected in stone tool assemblages, we must take into account the characteristics of the lithic resources, their distribution across the landscape, the characteristics of the landscape itself, the distribution of other resources such as water and food, and human strategies of mobility and resource exploitation. The assemblage from one layer of a Middle Palaeolithic rock shelter site, the Bau de l'Aubesier, shows that raw materials from different areas were used in different ways: they are more or less common in the assemblage, and they are more or less likely to have been brought in as raw material and knapped in situ . Various factors may have influenced this pattern. Measures of terrain difficulty and energy expenditure, the raw material quality, and characteristics of the sources are woven together to determine the attractiveness of each source. This is then placed in the context of the geology and geography of the area to distinguish a ‘main’ or core territory from a more extended territory visited during longer trips. The results show the value of taking a geoarchaeological perspective, which sees nature and culture as inextricably intertwined.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".