Traumatic injuries and imperialism: The effects of Egyptian colonial strategies at Tombos in upper Nubia
Bibliographic record
Abstract
As circumstances of conquest change, leaders of empires must adapt their colonial strategies in order to be successful. One example of such modification in approach is the shift from Middle Kingdom to New Kingdom Egyptian colonial activities in Nubia. During the Middle Kingdom (2050-1650 BC) Egypt used aggressive military campaigns to subdue the strong Nubian polity at Kerma, resulting in the construction of fortresses and many victory stelae. In the subsequent New Kingdom period (1550-1050 BC) during which the Egyptian administration succeeded in occupying nearly all of Nubia, changes were necessary in conquest strategies. Diplomacy and cooperation may have replaced military action as mechanisms of control. This article investigates changes in imperial policy through the examination of traumatic injuries in human skeletal remains. Patterns of injuries in a sample from the site of Tombos, an Egyptian colonial cemetery in Nubia dating to the New Kingdom period, are compared with data on the patterns of injuries from Kerma, a cemetery dating to the Middle Kingdom period, published by Judd (2004). Analysis indicates a decrease in the level of traumatic injuries from Kerma to Tombos supporting the idea that through time the Egyptian administration modified their colonial strategy toward more nonviolent means. This article presents data on differences in the patterns of injury at Tombos and Kerma and explores possible explanations for this variation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".