Intention to Write, Intention to Teach: Vernacular Poetry and Pedagogy in Thomas Norton's <i>Ordinal of Alchemy</i>
Bibliographic record
Abstract
Connections made by scholars between language and alchemy generally focus on the enigmatic or obscure technical jargon used by alchemists throughout alchemy's extensive history. Only occasionally do critical studies of medieval alchemical texts examine these works for their contribution to the canon of medieval vernacular literature or literary theory. Not surprisingly, scholarly discussions of alchemical writing in Middle English literature focus primarily on Chaucer. As recently as a 1999 article in the Chaucer Review, Mark J. Bruhn in "Art, Anxiety, and Alchemy in the Canon's Yeoman's Tale" discusses alchemy as "a metaphor for Chaucer's poetry." "[W] e should have no difficulty," say_s Bruhn, in "construing the ground of the metaphor between Chaucerian letters and alchemical multiplication" (p. 309). Jane Hilberry in a 1987 article on the technical language of the Canon's Yeoman's Tale argues that alchemy's "primary attraction lies in the language that surrounds the practice." She concludes her article: "While Chaucer in the Canon's Yeoman's Tale confirms alchemy's failure to change base metals into gold, he succeeds in transmuting the language of alchemy into poetry" (p. 442). We do find, then, an effort by medievalists to explore the relationship between language and alchemy in English literature, albeit seemingly limited to an interest in Chaucer's poetry rather than in his specific use of the English language.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".