Envisioning how fair use and fair dealing might best facilitate scholarship
Bibliographic record
Abstract
ABSTRACT Copyright law grants exclusive rights to authors of original works of authorship, but those rights are subject to numerous exceptions and limitations, including fair use in the United States and fair dealing in Canada. These exceptions have traditionally worked to ensure that the rights of copyright owners are adequately balanced with the interests of subsequent authors, researchers, and consumers of copyrighted works. Moreover, fair use has emerged as the most promising legal mechanism for the digitization, preservation, and study of large collections of copyrighted work. Fair use and fair dealing provide much of the flexibility needed to ensure that copyright protection serves to facilitate scholarship rather than threaten it. Scholars encounter copyright law both as authors and as users of copyrighted works. With an eye toward the future, this panel will examine the extent to which the discourses and practices of the past decade have contributed to shaping and reshaping our scholarly environment, how the information field has responded, and why and how information scholars, researchers and professionals ought to remain engaged in these matters in the future.
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 | 0.148 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.131 |
| Scholarly communication | 0.054 | 0.069 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".