Risk, Reality, Regulations? Finding what’s reasonable in copyright guidance
Bibliographic record
Abstract
Copyright guidance at an academic library is often provided at the nexus of the law, University policy, and the personal and professional values of the librarians and users involved in the decision making. An institution’s tolerance for risk (or lack-thereof) can create tension with librarians’ value systems. The law is often vague, leaving lots of room for differences of interpretation between University administrators, librarians, and users. Professional values, like the ones articulated by the ACRL Framework generally align with enhancing/supporting user’s rights and tend towards a copyleft point of view. Institutional risk tolerance complicates decision making further. Higher levels of risk are generally accepted with research and teaching endeavours, directly in contrast with a lower level of risk acceptable when it comes to compliance with the law (like the Copyright Act). Lack of clarity in the law and institutional risk tolerance can be at odds with professional values, which can confuse users and undermine librarians providing guidance. This article provides a beginning framework for understanding reasonableness in copyright decisions while taking into account the variety of pressures on copyright librarians. A set of cases are used to test the framework and a reasonableness chart is provided to allow for comparison of the cases.
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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 | 0.084 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.090 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.021 | 0.015 |
| 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".