Characterizing Journal Access at a Canadian University Using the Journal Citation Reports Database
Bibliographic record
Abstract
This article outlines a simple approach to characterizing the level of access to the scholarly journal literature in the physical sciences and engineering offered by a research library, particularly within the Canadian university system. The method utilizes the “Journal Citation Reports” (JCR) database to produce lists of journals, ranked based on total citations, in the subject areas of interest. Details of the approach are illustrated using data from the University of Guelph. The examples cover chemistry, physics, mathematics and statistics, as well as engineering. In assessing the level of access both the Library’s current journal subscriptions and backfiles are considered. To gain greater perspective, data from both 2003 and 2008 is analyzed. In addition, the number of document delivery requests, received from University of Guelph Library users in recent years, are also reviewed. The approach taken in characterizing access to the journal literature is found to be simple and easy to implement, but time consuming. The University of Guelph Library is shown to provide excellent access to the current journal literature in the subject areas examined. Access to the historical literature in those areas is also strong. In making these assessments, a broad and comprehensive array of journals is considered in each case. Document delivery traffic (i.e. Guelph requests) is found to have decreased markedly in recent years. This is attributed, at least in part, to improving access to the scholarly literature. For the University of Guelph, collection assessment is an ongoing process that must balance the needs of a diverse group of users. The results of analyses of the kind discussed in this article can be of practical significance and value to that process.
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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.138 | 0.141 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".