Reusing Figures in Research: New License Clauses Eliminate Need for Permissions or Payments
Bibliographic record
Abstract
Introduction: Academic authors typically seek permissions and pay a fee if they want to reuse figures, images, tables, or other brief excerpts from previously published works in their own publications. This process can be time-consuming and costly, presenting a real barrier for authors. However, in many cases, this convention may be unnecessary. Electronic resource (e-resource) licenses that libraries sign with publishers often now include clauses that permit the use, with appropriate credit, of such content from licensed materials by authorized users in their own publications for personal, scholarly, or educational purposes. Description of Program: This paper describes a project that we undertook to investigate which of our library’s current licenses include such a clause. We focused on licenses for the largest journal collections, likely the content most often cited by authors. Of the 23 licenses we reviewed, 19 had clauses permitting reuse. We anticipated that informing authors about this topic would be a challenge, so we thought carefully about strategies, developing a comprehensive communications plan to guide these efforts. Finally, we discuss several cases of authors helped by this project and how we shared our learning with colleagues in other libraries. Next steps: We plan to add this clause to our list of recommended licensing terms, incorporating it into more of our direct licenses with vendors from now on. Also, because many authors who we support are graduate students writing their theses or dissertations, we hope to empower student authors in considering the fair dealing copyright exemption for reusing figures in the future.
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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 | 0.077 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.153 | 0.118 |
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".