CARL Libraries – a Canadian resource-sharing experience
Bibliographic record
Abstract
Purpose – This study aims to map the current resource-sharing environment in Canada through the lens of its research libraries in general and the University of Alberta in particular. The findings present an interesting view of changing resource sharing patterns and trends. Design/methodology/approach – Interlibrary loan (ILL) transaction data were compiled from annual data reported to the Canadian Association of Research Libraries (CARL) and a case study of the University of Alberta is presented. Findings – The current trend shows declines in both borrowing and lending transactions. Research limitations/implications – Validity of the CARL ILL transactional data is subject to consistency in institutional reporting and accuracy of the data. The trends portrayed in the data are deemed realistic of the Canadian experience. Originality/value – This is an original study of CARL ILL transactional data, providing an aggregated view of 13 years of annual data, and an analysis of this data. It updates previous research and benchmarks current ILL patterns at CARL institutions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".