Bibliographic record
Abstract
Purpose The purpose of this paper is to address the issues associated with electronic resources management (ERM) system implementation in a consortium environment. Design/methodology/approach The paper outlines the implementation process along with the problems encountered and their solutions and impacts on the use of the system in the implementation of Verde ERM system at University of Windsor Leddy Library, which implemented the system as one of the early adopters within a consortium. The issues and challenges the library experienced in the project are analyzed and discussed. Findings The ERM system is still in its early stages. There are both benefits and challenges of the consortia approach in ERM system implementation. Should a library adopt the system within a consortium or just as a single library? When would be the right time to implement an ERM system? Answers depend on the library's local needs, resources and environment. The strategy of ERM system selection, evaluation and implementation is crucial for libraries to make a suitable decision. Practical implications The issues related to the ERM system implementation in a consortium environment discussed in the paper will have implications for libraries to select a proper approach and time on the adoption of emerging library systems. Originality/value The paper addresses issues related to a large library system, especially ERM system implementation in a consortium environment. The experience and findings obtained from the project can provide practical information to libraries that are considering implementing ERM or other large library systems.
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.
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 | 0.063 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".