Bibliographic record
Abstract
This article offers a series of experiments exploring the potential for ‘distant reading’ in French music criticism. ‘Distant reading’, a term first coined by literary theorist Franco Moretti, refers to quantitative approaches that allow for new insights into a large corpus of texts by aggregating data. While the main corpus employed here is the Revue et gazette musicale de Paris (1831–1877), I also use secondary corpora of reviews of Félicien David's Herculanum in 1859, Berlioz's reviews of Gluck and Beethoven in the Journal des débats and reviews that mention Gabriel Fauré in the Library of Congress’ Chronicling America database. My experiments employ a text analysis tool named Voyant, built by Geoffrey Rockwell and Stéfan Sinclair, thereby also offering a basic introduction to the range of visualizations employed in distant reading. My experiments focus on areas in which quantitative methods are particularly well suited to generating new knowledge: corpus-wide visualizations and queries, moving beyond traditional text searching, investigations of music critics’ authorial styles and detecting sentiment in reviews, and finally, to geographies of music criticism.
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 | 0.012 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".