Bibliographic record
Abstract
Abstract My 2000 monograph on the criteria for authenticity in historical-Jesus research analyzed the major criteria that have been used in such discussion. I also developed three new Greek-language based criteria, and, using these criteria, examined a number of important passages in the Gospels where it is useful to discuss their authenticity in Greek. The critical results have proved significant, with many scholars accepting that I have shown the likelihood of Jesus speaking Greek, even if they do not agree that I have convincingly proven every example where he did so. In that discussion, I did not treat Lk. 17.11-19, the episode of the cleansing of the ten lepers, in which one, a Samaritan, speaks to Jesus. In this paper, I apply the criterion of Greek language and its context to the episode and show that the persons involved and their backgrounds indicate that Greek may well have been the—or at least a—language in which they could and/or would have communicated.
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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.096 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| 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".