The Fallacy of 'True and False' in Prophecy Illustrated by Jer 28:8-9
Bibliographic record
Abstract
The study of ancient Near Eastern prophecy has shown that a principal distinction between ‘prophecy of salvation’ and ‘prophecy of judgement’ is questionable. The prophets in the ancient Near East did much more than just speaking pleasant words to those who paid them. Encouragement, although taking a prominent position in ancient Near Eastern prophecy, was accompanied by divine claims. Both in Mari and in Assyria, we see that if such claims were not granted or if a king had otherwise not fulfilled his duties, the gods, through their prophets, could reproach him. More drastically, prophecy of encouragement could be turned upside down. Whereas normally the gods encouraged the king and an- nounced the annihilation of his enemies, announcements of annihi- lation could also be directed against the king as part of a declaration of divine support to his adversary. The same prophetic voice that encouraged and legitimized the king, could also formulate demands on him, or even choose the side of his adversaries. The fact that prophets functioned within the existing order did not mean that they always agreed with the king and his politics. The interest of the cosmic and social-political order could well transcend the interests of an individual king.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".