Bibliographic record
Abstract
Abstract It has long been taken for granted that the use of εἴδωλον in the language of the Septuagint and its reception in early Jewish texts was unique within the history of postclassical Greek, imbued with pejorative meaning as a technical term of “Jewish Greek” that refers not to “images” but to “idols.” In this article, I interrogate the issues involved with this lexicographical commonplace and the translation practices it informs, and offer a critical reexamination of the literary and documentary evidence in which εἴδωλον occurs. I argue that the use of εἴδωλον in early Jewish Greek literature, from the Greek Pentateuch to Paul’s letters, was in fact remarkably unexceptional and readily recognisable as one lexical option among many in the diverse lexicon of cult images (e.g., ἄγαλμα, ἀνδριάς, ἀφίδρυμα, εἰκών, ἵδρυμα, ξόανον), just as it was in postclassical Greek more broadly. I conclude that transliterating εἴδωλον as “idol,” rather than translating it as “image,” keeps the illusion of uniqueness alive by inviting negative associations that closely align with its later lexicographical legacy but that do not clearly correspond to any known use of εἴδωλον in its own time and place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".