“In the Shadow of Greater Events in the World:” The Northern Epic in the Wake of World War II
Bibliographic record
Abstract
ABSTRACT: World War II was marked by widespread use of heroic narratives, national legacies, and grand ideas about destiny or the “arc of history.” These topics have a firm foundation in medieval literature, particularly in northern traditions. While literary medievalism had been in the limelight during the nineteenth century, during the early twentieth century it had been dismissed as a quaint curiosity; suitable for the benighted souls of the reading public, perhaps, but not to be taken seriously by avant-garde intellectuals. In the mid-twentieth century, however, literary medievalism returned with a vengeance. Questioning the critical narrative of twentieth-century literary history, this article examines iconoclastic works by Halldór Laxness (Iceland), T. H. White (England), John Gardner (America), and the Strugatsky brothers (Arkady and Boris, Russia), in order to compare perspectives on medievalism from different countries in the aftermath of the bloodiest conflict of all time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".