Exercices de l’âme fidèle. La littérature de piété en prose dans le milieu réformé francophone (1524-1685)
Bibliographic record
Abstract
source data for a computerized database, where the user would easily be able to find the small set of data they were specifically looking for.By putting this information in book form, the reader is forced to confront far more information than the output of a search engine.Scanning through the long lists of names provides ample opportunity for serendipitous discovery in a way databases cannot offer.Books are bulkier and more cluttered in their transmission of information than computers, but this clutter also carries valuable information; it may not have direct or immediate use, but it is information nonetheless.In its execution, Ars Epistolica reminds the reader that while computers are useful tools, they have their limits as a medium of research.Ars Epistolica is unquestionably a reference work featuring a bibliographic catalogue of the art of letter writing in sixteenth-century Europe.Even in appearance, this seven-hundred-plus page folio-sized hardcover impresses the fact that this book is not light reading.Nor is any but the most specialized historian likely to make daily use of this work.As a result, Ars Epistolica will not likely find its way into many personal collections; however, any scholar of sixteenth-century Europe would do well to consult this book from time to time.For this reason, Ars Epistolica is a fine acquisition for any collection at an institution where sixteenth-century scholars are found.aaron miedema
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".