Bibliographic record
Abstract
n o 2 / 2018 la redcouverte des catalogues d'diteurs qubcois Histoire du livre et bases de donnes bibliographiques Comment est n le projet de base de donnes ? Dveloppe pour rendre compte des phnomnes lis l'crit, tant dans leurs dimensions culturelles qu'conomiques ou politiques, l'histoire du livre a toujours privilgi les approches quantitatives. Les premiers travaux de Daniel Mornet sur Les Origines intellectuelles de la Rvolution franaise (1933), ou de Lucien Febvre et Henri-Jean-Martin sur L'Apparition du livre (1999-1958) traduisaient dj la volont d'apprhender de vastes corpus plutt que de se limiter quelques titres canoniques. Mais l'utilisation de bases de donnes, partir des annes 1990, a littralement transform la discipline en facilitant le traitement quantitatif et en multipliant ses possibilits.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".