Bibliographic record
Abstract
Is't not a fine sight, to see all our children made interluders? Do we pay our money for this? We send them to learn their grammar, and their Terence, and they learn their play-books? Ben Jonson, The Staple of News , Third Intermean, lines 42–4 Before the days of universal compulsory education, a fairly recent phenomenon in Western societies, a minority among European national populations was literate, and in England in Shakespeare's and Jonson's time only a small proportion of those male children who were taught first to recognize letters, then more complex reading and to write, went on to the rigorous process of ‘learning their grammar’, in Latin, beginning at around the age of eight, and continuing for about eight years. Following the completion of grammar school an even smaller percentage of adolescent young men might then attend one of the two universities for several years, many of them leaving without taking a degree, the last path followed by Thomas Heywood, Thomas Middleton, and perhaps also Ben Jonson. Other dramatists – Christopher Marlowe, Robert Greene, John Lyly, George Peele, and John Fletcher, to take fairly eminent names – held Oxford and Cambridge degrees, while Francis Beaumont studied at Gray's Inn, one of the inns of court, collegiate communities of lawyers and law students in London which collectively have been called England's third university of the period, and which, incidentally, provided a significant source of patronage for the commercial theatre of the city.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".