A spatial and chronological examination of butchering skill in the Levantine Early Bronze Age: analysis of the butchery marks from Tel Arad, Israel.
Bibliographic record
Abstract
Productive specialization is a component of all models for the evolution of complex and urban societies. However, it is difficult to measure this in terms of food provisioning. In any settlement or society, food provisioning is essential. In this thesis, I test the assumption that food provisioning will become more and more specialized as a society becomes more complex and consumers become more and more divorced from growing their own crops and raising their own animals. One aspect of food provisioning is animal butchering, and whether skilled or unskilled individuals are butchering animals. In a situation where there is household butchering, it is expected that the butchers will be unskilled. In contrast, where butchering is taking place on a large and regular scale, it is expected that the butchers will become more and more skilled. This thesis uses Butchering Incidences to quantify the nature of the butchered specimens and Butchering Mark Frequency as a weighted measure of butchering efficiency and skill. The faunal remains from the Early Bronze Age site of Tel Arad, Israel are used to test whether butchering skills change as the site evolves from an open-air settlement to a walled regional urban centre. The results indicate that all butchery activities for sheep and goats were conducted by relatively unskilled individuals over time at the site. There was not difference across the site as well. In contrast, the low Butchering Mark Frequency values for cattle disarticulation possibly suggest that they were butchered by individuals with higher skill levels. In general, however, most of the other cattle butchery activities were also conducted by lower skilled individuals. In general, the results from the site do not support a model of increasing specialization in food production as the site evolved into an early urban centre.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".