The Google Books Settlement: A Private Contract in the Absence of Adequate Copyright Law
Bibliographic record
Abstract
Internet search giant Google Inc. began digitizing library collections in 2004, confident that scanning and indexing books to display excerpts based on users’ search queries were fair uses under U.S. copyright law. Authors and publishers disagreed, and in 2005 representatives filed class action copyright infringement complaints. Rather than litigate, the parties negotiated a settlement that would not only allow Google’s original uses but license Google to use, and sell online, millions of books published before January 5, 2009. This report uses the experience of Canadian scholarly publisher the University of British Columbia Press to illuminate the November 13, 2009, proposed amended settlement agreement’s technical details, and it examines the settlement’s economic and cultural costs and benefits and its implications for digital publishing, public access, and copyright law in a rapidly developing digital market. Whatever this settlement’s outcome, its proposal underlines the need for meaningful, legislative copyright reform capable of encompassing present technological realities.
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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.036 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.025 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".