Bibliographic record
Abstract
This is the French novel corpus for the ELTeC, the European Literary Text Collection, produced by the COST Action Distant Reading for European Literary History (CA16204, https://distant-reading.net). The current version is v1.0.1. An overview over the authors and works represented in the collection can be gained here: https://distantreading.github.io/ELTeC/fra/index.html. Contributors Collection editors: Christof Schöch and Lou Burnard Contributors: Pia Geißel, Rezearta Murati, Evegnia Fileva Sources: Bibliothèque nationale de France (Gallica), Ebooks libres et gratuits / Bibliothèque électronique du Québec, CLiGS textbox, Wikisource, Bibebook.com, Atramenta, OBVIL, Project Gutenberg. Licence All texts included in this collection are in the public domain. No claim to copyright or similar protections is made for the composition of the corpus, the collection and presentation of the metadata, or the transcription and encoding of the texts. Citation suggestion If you use this corpus in your research or teaching, please follow good scholarly practice and use the following citation suggestion to acknowledge your source: French Novel Corpus (ELTeC-fra), edited by Christof Schöch and Lou Burnard. Version v1.0.1, April 2021. In: European Literary Text Collection (ELTeC). COST Action Distant Reading for European Literary History. DOI: https://doi.org/10.5281/zenodo.4662433 Release v1.0.1 Release v1.0.1 brings minor improvements to corpus metadata (and data on translations).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.213 |
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".