Bibliographic record
Abstract
comptes rendus 223 humanists were incredibly mobile across Italy, and many drew their identity not from geographical locations but from participation in a republic of letters (which he compares to scholars interacting on Twitter today), Celenza's analysis of Renaissance intellectuals as participants in a wider civic life could have been strengthened by closer analysis of the idiosyncrasies of the Renaissance civic and/or institutional spaces that created and fostered these intellectuals outside of Florence.Celenza has accomplished an impressive feat with this book.Most of his sources are easily and widely accessible (and, thankfully, in footnote format!), and he very helpfully leads the reader through the workings of Renaissance Latin with his translations in the text itself, introducing the machinations of Latin to the reader.His many asides of "quick parentheses, " found throughout the work, are useful and explanatory for understanding the influence of ancient and medieval philosophy that the humanists drew on, and he presents the latest research of the field in a very succinct format.In linking his episodic chapters with the wider question of Latin versus Italian, Celenza presents a rich analysis and narrative of what it meant to participate in Renaissance Italian intellectual life.I recommend his book-either as a whole, or individual chapters as essays-to undergraduates studying intellectual life during the Florentine Renaissance, or to graduate students and early researchers, as a robust and very clear introduction to Renaissance intellectual life and Renaissance humanism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".