Bibliographic record
Abstract
The middle time: The scholarly discovery of post-classical Latin and Greek In parallel with the scholarly discovery of the vernacular languages of Europe in the sixteenth and seventeenth centuries was that of post-classical Latin and Greek. There were several advantages to working on these language varieties rather than on their classical predecessors. One was that the period between antiquity and the fourteenth or fifteenth century offered an abundance of texts. A glance at, for instance, the five hundred quarto pages of Philippe Labbé’s Nova bibliotheca mss. librorum of 1653 shows how many post-classical inedita lay waiting for their editors, the manuscripts adequately catalogued and in major libraries, at a time when the supply of classical inedita had dwindled almost to vanishing-point. For a while, this abundance was strangely invisible to scholars looking for classical material – hence, for instance, Henri Estienne’s decision to use the Bibliotheca of Photius as a quarry for classical texts rather than to edit it as a whole. But, gradually, the merits of post-classical literature, together with the wealth of inedita , encouraged editors and therefore lexicographers to overcome their prejudices. This was all the more easily done because a classical training offered an immediate entrance to these texts. Their language was not purely classical – which is why they were of interest in their own right to lexicographers – but it was usually close enough to the classical for the divergences to be challenging rather than baffling.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".