Bibliographic record
Abstract
In our current era of disinformation, ready access to trustworthy sources is critical. “Fake news,” sophisticated disinformation campaigns, and propaganda distort the common reality, polarize communities, and threaten open democratic systems. What citizens, journalists, and policymakers need is a canonical source of trusted information. For millions, that trusted source resides in the books and journals housed in libraries, curated and vetted by librarians. Yet today, as we turn inevitably to our screens for information, if a book isn’t digital, it is as if it doesn’t exist. To address this gap, the Internet Archive is actively working with the world’s great libraries to digitize their collections and to make them available to users via controlled digital lending, a process whereby libraries can loan digital copies of the print books on their shelves. By bringing millions of missing books and academic literature online, libraries can empower journalists, researchers, and Wikipedia editors to cite the best sources directly in their work, grounding readers in the vetted, published record, and extending the investment that libraries have made in their print collections.
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 | 0.032 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.022 | 0.044 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".