Obscene Activity: Rethinking Agency and Desire in Early Medieval England
Bibliographic record
Abstract
The Exeter Book, a tenth-century collection of Old English poetry, contains ninety-odd riddles describing human interactions with objects, abstract concepts, and the natural world. Significant recent criticism has focused on how these riddles attribute agency, movement, or power to the nonhuman entities they describe, thus apparently decentering the human in accordance with twenty-first-century posthuman ethics. Yet this article, focusing on the obscene riddles of the collection, argues that although these poems do indeed attribute agency to nonhuman actors, this attribution does not always elevate these nonhuman actors so much as devalue “agency” as we understand it today. After exploring the pitfalls of reading for agency in Riddle 12 (Ox), I turn to an alternative method of attributing intention used in six of the collection’s other obscene riddles: “willa.” This concept, best translated here as “desire,” is a quality that works—like agency or animacy today—to draw and redraw the lines of the human in these double entendre poems. Using the work of Mel Y. Chen and Eunjung Kim, this article argues that by contextualizing agency and its historical equivalents in our analysis, we are better able to track violent power dynamics at work in early medieval English texts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".