Bibliographic record
Abstract
This article is a preliminary investigation of three decades of robbery, collection and trade in antiquities by General Moshe Dayan, perhaps Israel’s most famous commander and politician. In trying to separate facts from the many rumors that follow his name, this contribution is mainly based on written sources, some never published before. They prove that, since 1951, Dayan was involved in large-scale robbery of antiquities in dozens of sites in Israel and the occupied territories. Dayan used army equipment and personnel for robbery and transfer of antiquities; established a vast collection of stolen and bought antiquities, and exchanged and sold antiquities in Israel and abroad. He became a negative model for others and damaged the cause of Israeli archaeology as a whole. Although Dayan was caught in person at least four times during robbery, he was never brought to justice. After his death, his collection was sold by his widow to the Israel Museum for 1 million US$. Though Dayan’s activities are a sort of a known secret in Israel, they were never investigated from an archaeological perspective. Many facts remain unknown since they appear in remote Hebrew sources, hence writers about Dayan, including some of his biographers, often follow the wrong, romantic view of him as the ‘good guy’- a sort of an Israeli Robin Hood that fights stupid bureaucracy and social rules. This article brings a representative sample of Dayan’s deeds and tries to evaluate them and to ask how they were possible, and what has changed since those “good old days”.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".