eBook Loans: an e‐twist on a classic interlending service
Bibliographic record
Abstract
Purpose In April 2007, the Canada Institute for Scientific and Technical Information (CISTI), in collaboration with Ingram MyiLibrary, launched the eBook Loan Service. The paper describes the management of challenges associated with the project as well as the background and context of the aims to eBook Loan Service model. Conclusions and future activities by the partners with regard to e‐book lending are discussed. Design/methodology/approach The paper addresses two main topics: how the eBook Loan Service model was developed, the challenges and risks, the outcomes and benefits; and to evaluate whether a project stretching across boundaries of geography and time as well as between public and commercial partners can be managed successfully. Through a literature review, the context of the e‐book lending model for libraries is addressed, as well as the challenges of virtual project management. Findings The challenges and risks associated with implementing the new service were resolved and the project was a success. Originality/value The new service delivered by this project underlines the richness of new ideas emerging in the library community to improve access to scholarly literature in the digital age. With this model of affordable short‐term access to scholarly e‐books, libraries will be in a better position to serve the just‐in‐time needs of users in the electronic environment and end‐users will have better access.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".