Bibliographic record
Abstract
I'll admit that I have not always thought of licensing as a strategy to achieve the information policy goals of ARL and our member libraries.After all, licensing is not a traditional public policy and advocacy tool in the same way as lobbying lawmakers, influencing courts through amicus briefs, or commenting on proposed regulations.My view of the utility of licensing as another public policy tool shifted as I saw the authors of this book pioneer exemplary licensing strategies to protect the rights of researchers conducting computational analysis, and to ensure that students with disabilities can access materials needed for coursework and research at the same time as their peers.I'm so pleased that ARL could be involved in publishing this guide, written by some of the most generous licensing librarians in the field.Leading ARL's information policy and advocacy work involves staying on top of how licenses are affected by publisher business practices, and the legal and regulatory environment in the US, Canada, and internationally.I've been grateful for the opportunity to follow the work of this guide's authors as they align their libraries' licensing language with European legal standards, which have stronger protections for researchers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.239 | 0.155 |
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".