How Much Is Enough? Examining Computer Science and Civil Engineering Citation Data to Inform Collection Development and Retention Decisions in Three Large Canadian University Libraries.
Bibliographic record
Abstract
Science and engineering libraries have an important role to play in preserving the intellectual content in research areas of the departments they serve. This study employs bibliographic data from the Web of Science database to examine how much research material is required to cover 90% of faculty citations in civil engineering and computer science. Bearing in mind the importance of access to current as well as past research, as well as the issue of space in libraries, the study evaluates citations from one year's worth of research output from faculty in three prominent Canadian universities with departments in civil engineering and computer science: University of Toronto, University of British Columbia and McGill University for the purpose of best aligning collection development activities with science and engineering research needs. The findings for all three institutions combined show that 25 years of computer science literature is needed to cover 90% of researchers' citations, whereas 30 years of materials are needed for civil engineering. We also found that the citation data is not only discipline specific, but also location specific, and a one-size-fits-all approach is not appropriate when making collections and retention decisions. [ABSTRACT FROM AUTHOR]
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 | 0.017 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.037 | 0.116 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".