Jennifer Neville, <i>Truth is Trickiest: The Case for Ambiguity in the</i> Exeter Book <i>Riddles.</i> Toronto: University of Toronto Press, 2024, 376 pp.
Bibliographic record
Abstract
As the title indicates, this study focuses on the Old English riddles of the Exeter Book , with a particular emphasis on the ambiguity one faces when approaching these texts. The 95 riddles of the Exeter Book differ from the Anglo-Latin riddling tradition, out of which they emerged, in that they are unaccompanied by written answers. Jennifer Neville sees in this an advantage, rather than a limitation. She suggests that this lack of explicit solutions creates a different riddling experience, in which the goal was not the methodical finding of a definite solution, but rather the enjoyment of sustained thought and debate across a range of possible answers. Neville demonstrates the value of this approach throughout her book, with a particular emphasis on the ways that these riddles use various tropes to inform, trick, or mislead their readership.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".