HathiTrust: Ten Years, 16 Million Volumes, and the Road Ahead
Bibliographic record
Abstract
In 2008, the HathiTrust was launched with the mission: "To contribute to research, scholarship, and the common good by collaboratively collecting, organizing, preserving, communicating, and sharing the record of human knowledge." Now in its tenth year, HathiTrust has evolved into an organization of over 125 research libraries partnering to develop services and and a range of transformative programs enabled by working at a very large scale. A sketch of the HathiTrust profile reveals a trusted digital preservation service enabling the broadest possible access worldwide, including over 16 million total digitized items (volumes), 7.7 million book titles, 428,000 serial titles, over 1 million U.S. federal government documents, and a copyright review program that has contributed to the determination of 5.9 million items for open viewing via public domain status or Creative Commons licensing. To further leverage the benefits of HathiTrust as a large-scale digital library, the partnership is developing a massively-scaled shared print program and continues to build out the HathiTrust Research Center, which is positioned to offer scholars high-performance computational access to the 5.5 billion pages of text from the digital library for visualization, analysis, and research. This session will offer an overview of HathiTrust, its current programs, strategic directions, and the significant challenges it is undertaking.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.023 |
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".