The Impact of E-Readers and E-Books on the Library of Congress and the US Copyright Office
Bibliographic record
Abstract
Consumer acceptance of e-readers, tablets, and e-books has been covered extensively by the press. These trends have been a significant business opportunity for some authors, publishers, e-reader or tablet manufacturers, or distributors because a new market has emerged and ‘new’ readers have been attracted to the portability and the price of e-readers and e-books. But there is a ‘dark side of the moon.’ E-readers and e-books have emerged as ‘disruptive technologies,’ resulting in a reduction in the number of book outlets and printed books sold in the United States. In this research paper, we investigate the current and potential impact of e-books on the Library of Congress and the US Copyright Office, including the budgets, staffing, and operations of the Library of Congress and the US Copyright Office, and the need to digitize the vast book collection of the Library of Congress. This paper presents a series of recommendations for both the Library of Congress and the US Copyright Office.
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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.039 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".