On Dispensing with Q?: Goodacre on the Relation of Luke to Matthew
Bibliographic record
Abstract
The case against Q depends logically on the plausibility of Luke's direct use of Matthew. Goodacre's carefully argued book contends (a) that none of the objections to the Mark-without-Q hypothesis is valid; (b) that given certain assumptions about Luke's aesthetic preferences, it is plausible that he systematically reordered the ‘Q’ material from Matthew; (c) that Luke's rearrangement of Matthew shows as much intelligence and purposefulness as Matthew's; and (d) that certain features of the ‘Q’ in Luke 3–7 betray the influence of Matthean redaction. Careful scrutiny of these arguments shows that (a) is only partially true; that Goodacre's assumptions about Lukan aesthetics (b) are open to serious objection; and that while (c) is true, Goodacre's argument in (d) ultimately cuts against his case against Q.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".