Essential Academic Journals Tend to Be of Universal Importance, While Many Journals Available on For-Profit Platforms Appear to Be Ancillary
Bibliographic record
Abstract
A Review of: Mongeon, P., Siler, K., Archambault, A., Sugimoto, C. R., & Larivière, V. (2021). Collection development in the era of big deals. College & Research Libraries, 82(2), 219–236. https://doi.org/10.5860/crl.82.2.219 Abstract Objective – (1) Present a method of journal appraisal that combines reference list, article download, and survey data. (2) Gauge journal usage patterns across selected universities. Design – Analysis of reference lists, article downloads, and survey data. Setting – 28 Canadian universities. Subjects – 47,012 distinct academic journal titles. Methods – Download data for the 2011-2015 period was sourced from standard Journal Report 1 (JR1) usage reports as supplied by the vendors. Download figures were summed for journals that were available through multiple platforms. Reference list data (i.e., the number of times documents published in each journal were cited by authors affiliated with a participating institution) was sourced from Clarivate Analytics’ Web of Science, limiting for the years 2011-2015. An unknown number of researchers at 23 of the 28 participating universities were invited by email to complete a survey. The survey asked respondents to list the scholarly journals they considered essential for their research and teaching (up to 10 journals for each purpose). The three datasets (download, reference list, and survey data) were then merged. Duplicates and non-academic journals were removed. Journals were then grouped into broad discipline areas. A list of “core journals” (p. 228) was created for each institution. These journals produce 80% of downloads, 80% of citations, or 80% of survey mentions at each institution. A journal only had to reach the threshold in one category (i.e., in either downloads, citations, or mentions) to make it onto the core journals list. A “low” (p. 228) survey response rate meant “one mention [was] generally enough" (p. 228) for a journal to be classified as core. Main results – Fewer than 500 titles (n=484, ~1%) made it to the core journals list at all 28 universities. Two thirds (66%, n unknown) of journals did not make it onto the core list of any university. Of the journals deemed to be core, most (60%, n unknown) were shared across all institutions. On average, platforms from not-for-profit organizations and scientific societies contain a higher proportion of core journals than for-profit platforms. Notably, 63.6% of Springer journals, 58.9% of Taylor & Francis journals, and 45.8% of Elsevier’s journals do not appear on the core journal list of any university. Conclusion – Libraries should consider ways to share resources and work more cooperatively in their negotiations with publishers. Further, libraries may be able to cancel entire journal bundles without this having a “sizable” (p. 233) impact on resource 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 | 0.014 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".