Bibliographic record
Abstract
Against the reference in Q to “crowds” generally, John the Baptist’s response in Matt 3:7 to “Pharisees and Sadducees” who arrive for baptism is, “Offspring of vipers [γεννήματα ἐχιδνῶν], who warned you to flee from the coming wrath?” In Matt 12:34, Jesus himself denounces the Pharisees as “offspring of vipers [γεννήματα ἐχιδνῶν].” Similarly in Matt 23:33, he demands of “scribes and Pharisees” alike, “Snakes, offspring of vipers [ὄϕεις, γεννήματα ἐχιδνῶν], how will you escape the judgment of Gehenna?” The fundamentally ambiguous and frequently negative overtones of serpent imagery elsewhere in Second Temple Judaism and the wider Mediterranean world, the prevalence of wordplay and its application to intercommunal Jewish polemic, and the broader function of texts as instruments of community definition together provide an appropriate series of contexts for understanding this invective. Notwithstanding the plausibility of such language on the lips of John and Jesus alike, these terms are singularly appropriate to Matthew’s own task of situating his community in relation to that of Pharisaic Judaism in the immediate aftermath of the first Jewish war. This study explores, first, analogous language from Hellenistic and Jewish (especially Dead Sea and Mishnaic) literature and, second, the possibility that behind the references to serpents lies an invidious wordplay on one or both of ספר, “scribe,” and פרושים, “Pharisees.”
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".