Key factors for consortial success: realizing a shared vision for interlibrary loan in a consortium of Canadian libraries
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the factors associated with the successful implementation of a shared interlibrary loan (ILL) system by the Ontario Council of University Libraries (OCUL), a consortium of 20 Ontario universities. Design/methodology/approach The paper is a descriptive review of the approaches taken in the consortial implementation of OCLC's VDX software. The paper elaborates on the building‐blocks and barriers to success as they were experienced, first by participants in OCUL's centralized implementation activities, and second at the local level by staff at Ryerson University Library, a member institution. Now end users can simultaneously search world‐wide catalogues, submit and track progress of requests, and receive materials rapidly. System functionality includes auto‐mediated interlibrary loans (direct requesting); use of link‐resolver software to transfer citation information from borrowing library catalogues to ILL request forms; and ISO peer‐to‐peer messaging. Findings Post‐implementation analysis reveals several key factors that contributed to the project's success. These include: planning, leadership, financial support, technical support, cooperation, staff commitment, communication, staff‐and end‐user centered focus, training and evaluation. Practical implications This may have broad application for similar complex projects. Originality/value The OCUL VDX implementation has achieved the originally expected economies of scale, service performance improvements and reduction in localized maintenance and system support. However, there have also been several unforeseen benefits such as the formulation and standardization of the OCUL ILL policies, and the development of Canada‐wide consortial reciprocal agreements. At the operational level, staff have had to adjust their management styles and develop confidence not only in their individual skills but also in cooperative thinking, reliance on centralized support, and in the overall system. Throughout the project the objectives have been clearly identified, and, for the most part, enthusiastically adopted, by consortium members. Recognizing that ILL is a service that is in transition, staff now look at business transformation and ways to identify, share and adopt best working practices.
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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 | 0.035 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".