Bibliographic record
Abstract
AULUS GELLIUS (b.c. ce125),1 in Book 3 of the Attic Nights, relates the following observation made by Sallust in The War with Catiline: ‘Avarice implies a desire for money, which no wise man covets; steeped as it were with noxious poisons, it renders the most manly body and soul effeminate …’2 The relevance of this observation to Chaucer's description of the Pardoner is apparent. At least since W. C. Curry's analysis, critics have associated the word ‘mare’ in the narrator's speculation, ‘I trowe he were a geldyng or a mare’ (I.691), with the possibility of effeminacy.3 Jill Mann observed a connection between the label ‘mare’ and the description of homosexual men as effeminate in a poem by Walter de Chatillôn.4 Monica McAlpine opines, in an article that has become the standard point of reference on the subject, that ‘ “Mare” must be a term commonly used in Chaucer's day to designate a male person who, though not necessarily sterile or impotent, exhibits physical traits suggestive of femaleness.’5 While McAlpine argues for the Pardoner's homosexuality, she acknowledges that effeminacy need not imply homosexuality and critics have continued to debate the implications of the description.6 Richard Firth Green, for instance, has argued that effeminacy is a sign not of impotence or homosexuality but rather of womanizing.7
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".