Making User Rights Clear: Adding e-resource License Information in Library Systems
Bibliographic record
Abstract
Libraries sign a wide variety of licensing agreements that specify terms of both access and use of a publisher's electronic collections.Adding easily accessible licensing information to collections helps ensure that library users comply with these agreements.This article will describe the addition of licensing permissions to resource displays using Mondo [1] by Queen's University and Scholars Portal (a service of the Ontario Council of University Libraries) [2] .We will give a brief introduction to Mondo and explain how we improved Mondo to add the license permissions to different library systems.The systems we used are an ILS (Voyager), an OpenURL Link Resolver (360 Link), and a Discovery System (Summon).However, libraries can use Mondo to add the license permissions to other library systems which allow user configurations.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.018 | 0.071 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".