The Routledge Handbook of Progressive Rock, Metal, and the Literary Imagination
Bibliographic record
Abstract
The Routledge Handbook of Progressive Rock, Metal, and the Literary Imagination illustrates the many ways that progressive rock and metal music forge striking engagements with literary texts and themes. Our authors and their objects of analytic inquiry offer global and diverse perspectives on these genres and their literary connections: from ancient times to the modern world, from children’s literature to epic poetry, from mythology to science fiction, and from esoteric fantasy to harsh political criticism. The musical treatments of these literary materials span the continents from South and North America through Europe and Asia. The collection presents critical perspectives on the enduring and complex relationships between words and music as these are expressed in progressive rock and metal. The book is aimed primarily at an academic market, valuable for second through final year students on undergraduate courses devoted to both popular music and to literary studies, and to postgraduate programs and researchers in a range of fields, including: popular music studies, musicology, creative music performance and composition, songwriting, literary studies, narrative studies, folklore studies, science fiction studies, cultural studies, liberal studies, and sociology, and for media and history courses that have an interest in the intersection of narratives, music and society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".