From Course Packs to AI: Evolving Author and Publisher Rights in Journal Publishing Agreements
Bibliographic record
Abstract
Introduction: This study analyzes the content of non-Open Access (OA) article publishing agreements from 15 publishers. Themes, commonalities, and differences are identified, as well as recommendations to publishers and salient aspects that librarians will find helpful when building copyright knowledge. Methods: In this study, 16 journal publishing agreements, a mix of large corporate publishers, scholarly presses, and associations, were examined, entered into a rubric, and analyzed in Excel. In 2003, Gadd et al. conducted a similar study; their method was used as inspiration and updated for today’s publishing landscape. Results: Findings show that a mix of copyright transfer agreements and licenses is used. Over two-thirds of the publishers in this study require authors to give up their copyright to be published without paying article processing charges. There is often little to no difference between exclusive licenses to publish and copyright transfer agreements in terms of rights retained by authors. One area of note is that some agreements allow the inclusion of authors’ works in AI products and tools without the notification or permission from the authors required. Conclusion: Publishers have adapted to today’s environment. Librarians should continue practicing copyright literacy in order to assist scholars to make informed decisions and remain current with publisher practices.
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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 | 0.029 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".