Bibliographic record
Abstract
We began to babble in Latin: Henri Estienne and the inheritance of languages Robert Estienne’s son Henri has been called ‘one of the dominant literary and scholarly figures of the second half of the sixteenth century in Europe … among the greatest French prose writers of the Renaissance … this giant of sixteenth-century scholarship … one of the most fascinating personalities of the Renaissance’. However, the scope and complexity of his achievement have, paradoxically, led to his being generally underappreciated. The most extensive work on him has been done with reference to one part, and not the largest, of his intellectual life, namely his works in French and his discussions of the French language. Beyond that, the best treatments have – unsurprisingly, given his colossal printed output – taken the form of bio-bibliographies, most recently the book quoted above, Fred Schreiber’s The Estiennes , which is the splendid catalogue of a collection of Estienne editions now at the University of North Carolina at Chapel Hill. So, no continuous biography of Estienne has replaced Léon Feugère’s of 1853, and the fullest account of his work in English apart from Schreiber’s is an essay by Mark Pattison which reviews Feugère’s work. Henri Estienne was born in 1531. By the time of his childhood, the family connection with the business of scholarly printing and publishing was even stronger than it had been at the time of his father’s.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".