MultiHATHI: A Complete Collection of Multilingual Prose Fiction in the HathiTrust Digital Library
Bibliographic record
Abstract
This dataset provides detailed metadata on ca. 10.2 million works of fiction and non-fiction written after 1799 in 521 different languages available in the HathiTrust Digital Library. The dataset bolsters the May 2022 Hathifile by supplying missing predicted fiction tags with a bespoke BERT-based multilingual classifier. Our classifier completes the catalogue with an additional 400,000 non-English volumes predicted to be works of fiction, capturing 95% of all works presently provided by HathiTrust. We provide each work with metadata including the work’s genre at the level of fiction or non-fiction, length in pages, original language, and the year the work was published. With a total page count of ca. 1.4 billion pages, our dataset provides researchers with a substantial source of non-English modern literature. We also present insight into how multilingual classifiers can be trained with monolingual data, itself a discovery with implications for the study of lower resource languages. We hope our provisions will accelerate empirical research into non-English prose and literature.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.027 |
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".