Analyzing and Assessing a Library Collection Using Faculty Citations Via OpenAlex and R
Bibliographic record
Abstract
Objective – Citation analysis is becoming a popular means of analyzing and assessing library collections due to its relatively unobtrusive nature and the growing accessibility of citation data. The primary goal of this study was to assess whether the library at the University of Scranton is successfully meeting the research needs of faculty based on analysis of faculty publication and citation data from OpenAlex’s application programming interface. Secondarily, this study analyzed faculty publication and citation patterns to help identify opportunities for the library to better support faculty in their research and publishing. Methods – This case study focused on a citation analysis of the University of Scranton’s faculty publications from 2013 to the present. Using OpenAlex and R computing language as non-proprietary sources of data and data analysis, faculty publications and citations were examined and compared to current library holdings. Results – Overall, 16,786 unique citations from 1,045 unique faculty publications were examined and compared to a list of current library holdings. Findings concluded that approximately 65% of citations were available through library holdings. Further analysis of faculty publication practices suggested that there are a growing number of faculty publishing open access which indicates that there may be additional opportunities to support faculty in this area. Conclusion – While this case study represented specific needs and use cases at the University of Scranton, the ultimate importance of this study is the process itself. The use of non-proprietary tools and data sources like OpenAlex and R create exciting new opportunities for others who wish to conduct similar studies at their own institutions without relying on proprietary tools and data sources or resorting to more labor-intensive methods.
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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.070 | 0.070 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".