Bibliographic record
Abstract
A comprehensive survey of one of the most important texts of the Middle Ages. Malory's Morte Darthur is now a canonical and widely-taught text. Recent decades have seen a transformation and expansion of critical approaches in scholarship, as well as significant advances in understanding its milieux:textual, literary, cultural and historical. This volume adds to and updates the influential Companion of 1996, offering scholars, teachers and students alike a full guide to the text and the author. The essays it contains provide a synthetic overview of, and fresh perspectives on, the key questions about and contexts connected with the Morte. MEGAN G. LEITCH is Senior Lecturer in English Literature at Cardiff University; CORY JAMES RUSHTON is Associate Professor in the Department of English at St Francis Xavier University, Canada. Contributors: Dorsey Armstrong, Thomas Crofts, Siân Echard, Rob Gossedge, Daniel Helbert, Amy Kaufman, Megan Leitch, Andrew Lynch, Catherine Nall, Ralph Norris, Raluca Radulescu, Lisa Robeson, Meg Roland, Cory Rushton, Masako Takagi, Kevin Whetter.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.046 |
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".