Bibliographic record
Abstract
Liver consultation was a practice in which the liver of an animal would be examined in order to receive messages from the gods. Ancient peoples would often perform this type of ritual before important decisions, such as committing to war, or signing a peace treaty. Throughout this paper, I examine three different systems of liver consultation from antiquity in chronological order—beginning with Babylonian extispicy since it is the most ancient, then moving to Greek hepatoscopy, and finishing with Etruscan haruspicy. As the names of these systems of liver consultation are different (extispicy, hepatoscopy, and haruspicy), so too are the practices of each culture. In order to highlight these differences, I analyse five aspects of each civilization: 1) Their religion, and the place of liver consultation within it, 2) The priests or diviners who performed the consultation, 3) The visual sources and material remains relating to liver divination, 4) The ancient literary accounts about this practice, and 5) The system utilized by the diviners in order to read livers. In an attempt to reconstruct the work of ancient diviners, I have undertaken a practical examination of sheep livers in the Babylonian, Greek, and Etruscan methods. I examined five sheep livers, each in all three methods, in order to determine the extent that divinatory procedure altered the message of the liver to the respective diviners. I conclude my paper by comparing the findings about each culture and relating my experience in the role of the diviner.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".