Science A&I Database Holdings at ARL and Oberlin Group Libraries, 2011–2016: A Longitudinal Study
Bibliographic record
Abstract
After instituting major cuts to discipline-specific science abstracting and indexing (A&I) databases at an ARL library due to significant budget cuts, the author sought to determine if such cuts were being made by other academic libraries and what trends could be found in holdings of such databases. Annually, over the course of eight years, 108 ARL libraries and 74 Oberlin Group library website A–Z database lists were reviewed to look for the presence of 21 Science and Technology A&I databases. Additions and cancellations were recorded and verified. The results indicate little change in the holdings of several discipline-specific databases including MathsciNet, SciFinder Scholar, and GeoRef, while there were declines in holdings of several other databases including INSPEC, Biological Abstracts, and Compendex. Also measured were holdings of Proquest and EBSCO science A&I databases, which saw small declines in holdings, as well as holdings of comprehensive A&I databases Scopus and Web of Science, which saw a significant increase for Scopus holdings.
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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".