The “poisonous water” (mê rʾōš) in Jer 8:14 and the “bewitched water” (mê kaššāpūti) in Maqlû i 103-104: witchcraft in the book of Jeremiah
Bibliographic record
Abstract
Some scholars consider the biblical phrase mê rʾōš (“poisonous water”) a metaphor for the venom of a snake, others interpret it as a poisonous substance produced by pressing herbs and still, others believe it to be a metaphor for the destruction of the people Israel and their land. In the book of Jeremiah in particular, the phrase mê rʾōš appears three times (8:14, 9:15, and 23:15) and in all cases, it appears in execratory contexts. Numerous studies have put this phrase in relation to the trial ordeal in Numbers 5:11-31, and have therefore recognized its execratory nature, yet, to my knowledge, no one has ever studied it against the background of the Neo-Assyrian magical tradition. Accordingly, the expression “poisonous water” may have magical nuances attached to it. For example, the ancient Mesopotamians believed that curses could be passed to the victim by means of food or drink. In this analysis, I argue that the expression mê rʾōš may have the function that the Akkadian phrase mê kaššāpūti (“bewitched water”) has in Assyrian anti-witchcraft rituals where the administration of a poisonous drink symbolized the nullification of a curse as it was believed that the bewitched potion given to the evildoer returned to him the evil he had intended for his victim. In my talk, I will analyze the theme of the transfer of the curse through liquids and food in select Assyrian literature. I will then show how the book of Jeremiah redeployed this Assyrian theme to articulate its theological offensive against the harmful effects of the oracular utterances of illegitimate Prophets.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".