MULER: Building an Electronic Resource Management (ERM) Solution at York University
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many university libraries now utilize an Electronic Resource Management (ERM) system to assist with operations related to electronic resources. An ERM is a relational database containing information such as suppliers, costs, holdings, and renewal dates for electronic resources, both at the database and title levels. While commercial ERM products are widely available, some institutions are custom building their own ERM in- house. This article describes how York University in Toronto, Canada, did just that by building a system called Managing University Library Electronic Resources (MULER). The article details the background and history of how electronic resources were managed pre-MULER; why a new ERM was needed; the planning process; the current and innovative functions of MULER, including integration of MULER data into York University Libraries search and discovery layer, Vufind; subject tagging in MULER; new functions to be added; and lessons learned from the project. Positive and negative implications of choosing an in-house project over paying for a commercial product are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it