Bibliographic record
Abstract
Lesbia’s Sparrow (Catullus 2) Jack Mitchell (bio) Sparrow, my darling’s pet, Often along her kneesShe leads you, so she’ll get Nibbled, when it may please My luminous desire To play and so assuageThose twinges that require Some easing of love’s rage. If only you’d prefer Instead of her with meTo play, and so I’d cure This intimate ennui. It’s like that girl assassin, How a golden flashOf apple could unfasten Such a long-cinched sash. [End Page 45] Passer, deliciae meae puellae,quicum ludere, quem in sinu tenere,cui primum digitum dare appetentiet acris solet incitare morsus,cum desiderio meo nitenticarum nescio quid lubet iocari,et solaciolum sui doloris,credo, ut tum gravis acquiescat ardor;tecum ludere sicut ipsa possemet tristis animi levare curas!…tam gratum est mihi quam ferunt puellaepernici aureolum fuisse malum,quod zonam soluit diu ligatam. (text of R.A.B. Mynors, 1958) [End Page 46] Jack Mitchell jack mitchell is an associate professor of Classics at Dalhousie University. His most recent work is an adaptation of the original Star Wars film trilogy as an 8000-line epic poem in blank verse, The Odyssey of Star Wars (Abrams, 2021). Since 2021, he has contributed a daily epigram to The Hub, an online Canadian magazine of ideas and public policy. Copyright © 2022 Trustees of Boston University
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.031 |
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".