MultiHATHI: A Complete Collection of Multilingual Prose Fiction in the HathiTrust Digital Library
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it