Bibliographic record
Abstract
Exploring the definition of ‘fair use’ as a facet of copyright law in the United States, Sanford Thatcher examines the inherent tension caused by this legal notion in scholarly publishing, particularly in the new electronic era. Libraries, and, by extension, universities, increasingly advocate a stronger assertion of fair use in higher education to cope with diminishing funds. University presses, in contrast, view broader definitions of fair use as a threat to the already decreasing market potential for scholarly monographs, despite recognizing that their interests are closer to the aims of higher education than they are to the aims of commercial publishers. As an example of this double-edged sword, Thatcher discusses photocopying and electronic journals and their economic and legal effects on publishers. Without due consideration of university presses and their mandate to publish scholarly research, aggressive fair use will constrain those publishers and adversely affect the careers of the authors fair use is designed to assist.
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 | 0.044 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.168 |
| Scholarly communication | 0.036 | 0.043 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".