Matthew and Mark across perspectives : essays in honour of Stephen C. Barton and William R. Telford
Bibliographic record
Abstract
Preface Abbreviations 1. Introduction, Kristian A. Bendoraitis, Spring Arbor University, USA 2. How Did Mark Survive? Francis Watson, University of Durham, UK. 3. Paragon of Discipleship? Simon of Cyrene in the Markan Passion Narrative, Helen K. Bond, University of Edinburgh, UK 4. 'More than a Prophet': Echoes of Exorcism in Markan and Matthean Baptist Traditions, Daniel Frayer-Griggs, Independent Scholar 5. 'He Laid Him in a Tomb' (Mark 15.46): Roman Law and the Burial of Jesus, Craig. A. Evans, Acadia Divinity School, Canada 6. The Newness of the Gospel in Mark and Matthew: Continuity and Discontinuity, Donald A. Hagner, Emeritus, Fuller Theological Seminary, USA 7. Emotions of Protest in Mark 11-13: Responding to an Affective Turn in Social-Scientific Discourse, Louise J. Lawrence, University of Exeter, UK. 8. The Spirituality of Faith in the Gospel of Matthew, Nijay K. Gupta, George Fox Evangelical Seminary, USA 9. Matthew - a Jewish Gospel of Jews and Gentiles - James D.G. Dunn, Emeritus, University of Durham, UK. 10. The 'Apocalyptic' Jewish Jesus and Contemporary Interpretation, Loren Stuckenbruck, Ludwig-Maximilians-Universitat, Germany Bibliography Indexes
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".