Bibliographic record
Abstract
Abstract The Synoptic Problem owes its birth to the complex array of agreements and discrepancies that exist among the Synoptic Gospels: two, and sometimes three, of the Synoptics display a very high degree of verbatim agreement in the telling of some stories or sayings. In other cases, the agreement is very low. Matthew and Luke, for example, agree almost completely on the wording of the saying on God and Mammon (Matt 6:24 // Luke 16:13) but show wide disagreement in their infancy stories. Matthew, Mark, and Luke generally agree in the sequence of stories, especially after Mark 6:6. But significant discrepancies are also found in the relative sequence of other stories and sayings. While Matthew and Mark locate the anointing of Jesus during the last week of his life (Mark 14:3–9 // Matt 26:6–13), Luke locates it in the midst of Jesus’s activities in the Galilee (Luke 7:36–50).
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".