Bibliographic record
Abstract
Responding to the common plea in medieval inscriptions to ráð rétt rúnar, to ‘interpret the runes correctly’, this thesis provides a series of contextual readings of the runic topos in Anglo-Saxon and Old Norse poetry. The first chapter looks at the use of runes in the Old English riddles, examining the connections between material riddles and certain strategies used in the Exeter Book, and suggesting that runes were associated with a self-referential and engaged form of reading. Chapter 2 seeks a rationale for the use of runic abbreviations in Old English manuscripts, and proposes a poetic association with unlocking and revealing, as represented in Bede’s story of Imma. Chapter 3 considers the use of runes for their ornamental value, using 'Solomon and Saturn I' and the rune poems as examples of texts which foreground the visual and material dimension of writing, whilst Chapter 4 compares the depiction of runes in the heroic poems of the Poetic Edda with epigraphical evidence from the Migration Age, seeking to dispel the idea that they reflect historical practice. The final chapter looks at the construction of a mythology of writing in the Edda, exploring the ways in which myth reflects the social impacts of literacy. Taken together these approaches highlight the importance of reading the runes in poetry as literary constructs, the script often functioning as a form of metawriting, used to explore the parameters of literacy, and to draw attention to the process of writing itself.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".