Music, multimodality, and narrative viewpoint: <i>Beowulf</i> in performance
Bibliographic record
Abstract
Abstract This article examines a solo musical/dramatic performance of the Old English poem Beowulf . Drawing on recent literature on multimodal communication, conceptual blending, and music cognition, it specifically discusses musical means of constructing and manipulating narrative viewpoint, exploring how sound and embodied performance influence and create the meanings of verbal narrative. Blending analysis, focused as it is on the integration of disparate input concepts into one blended space and the meaning-making potential of this process, lends itself naturally to the interaction of various modes of communication. In Benjamin Bagby’s Beowulf , the embodied, preformative resources of oral storytelling—particularly musical sound—structure narrative viewpoint in ways not afforded by the text alone, and thus support the process of story-construction. The famously ambiguous “Unferð Episode,” a dialogic exchange incorporating complex viewpoint phenomena and embedded narratives, exemplifies how such manipulation of viewpoint constrains and guides interpretation. As this article demonstrates, Bagby’s storytelling tools—verbal delivery, musical organization, gesture, posture—do not merely communicate a sequence of events but define the ways characters, narrators, and audience members negotiate, view and construe the stories constituted by those events.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".