Bibliographic record
Abstract
disce, puer, uirtutem ex me uerumque laborem, fortunam ex aliis. nunc te mea dextera bello defensum dabit et magna inter praemia ducet. tu facito, mox cum matura adoleuerit aetas, sis memor et te animo repetentem exempla tuorum et pater Aeneas et auunculus excitet Hector. ( Aen . 12.435–40) Yes, yes, if you please, no reference to examples in books. Men have had every advantage of us in telling their own story. Education has been theirs in so much higher a degree; the pen has been in their hands. I will not allow books to prove any thing. (Jane Austen, Persuasion ) When Statius published the Thebaid in 92 CE, late in the reign of the emperor Domitian, he hoped to secure the lasting success of his epic by attracting imperial favour and by achieving a place in the Roman educational system. In an unusual envoi bidding farewell to his epic, Statius records indications of the present popularity of the poem as an index of its future acclaim: iam certe praesens tibi Fama benignum | strauit iter coepitque nouam monstrare futuris. | iam te magnanimus dignatur noscere Caesar, | Itala iam studio discit memoratque iuuentus (‘Certainly attendant Fame has already laid a benevolent path for you, and begun to show you, new as you are, to future generations. Already generous Caesar deigns to know you; already the youth of Italy learns you with zeal and recites you’, Theb . 12.812–15). Statius was neither the first nor the last in the long line of ancient epicists to aspire to a place in the classical curriculum on the model of Homer, whose poetry enjoyed pride of place in education throughout antiquity.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".