Measuring Scholarly Productivity of Long Island Educational Institutions: Using Web of Science and Scopus as a Tool
Bibliographic record
Abstract
Objective – This paper explores how to utilize two well-known library databases, Thomson Reuter’s Web of Science and Elsevier’s Scopus, to quantify Long Island educational institutions’ scholarly productivity. Methods – Institutions located in the Long Island region and within Nassau and Suffolk counties, including the State University of New York (SUNY) colleges, private institutions, and technical schools, were examined for the last 14 years (2000–2013). Eight Long Island institutions were represented in both databases and were included in the study. Results – Of the eight institutions, Stony Brook University produced the most publications indexed in Web of Science and Scopus during the period of 2000–2013. Cold Spring Harbor Laboratory yielded the second most publications during 2000–2013 in both Web of Science and Scopus, but it produced the highest quality publications compared with other institutions excluding Stony Brook University. Although the annual growth rates of Farmingdale State College and New York Institute of Technology increased dramatically in both Web of Science and Scopus, the large proportional increase did not represent a large increase in total value. Additionally, some institutions had a higher number of publications indexed in Web of Science than in Scopus, and others had a higher number of publications indexed in Scopus than in Web of Science. Conclusions – Data were collected from institutions in Long Island with various institutional sizes, the number of faculty members employed may have made an impact on the number of publications. Thus, publication data in this study cannot be used to compare their rankings. Institutions with a similar type and similar size of faculty members should be selected for comparison. Due to the different coverage and scope of Web of Science and Scopus, institutions should use both databases to examine their scholarly output. Furthermore, institutions should consider using altmetrics to capture various impacts of the scholarly output to complement the traditional metrics.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.055 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".