The Impact of Data Source on the Ranking of Computer Scientists Based on Citation Indicators: A Comparison of Web of Science and Scopus.
Bibliographic record
Abstract
Conference proceedings represent a large part of the literature in computer science. Two Conference Proceedings Citation Index databases were merged with Web of Science in 200: but very few studies have been conducted to evaluate the effect of that merger of databases on citation indicators in computer science in comparison to other databases. This study explores whether or not the addition of the Conference Proceedings Citation Indexes to Web of Science has changed the citation analysis results when compared to Scopus. It compares the citation data of 25 randomly selected computer science faculty in Canadian universities in Web of Science (with Conference Proceedings Citation Indexes) and Scopus. The results show that Scopus retrieved considerably more publications including conference proceedings and journal articles. Scopus also generated higher citation counts and h-index than Web of Science in this field, though relative citation rankings from the two databases were similar. It is suggested that either database could be used if a relative ranking is sought. If the purpose is to find a more complete or higher value of citation counting or h-index, Scopus is preferable. It should be noted that no matter which source is used, citation analysis as a tool for research performance assessment must be constructed and applied with caution because of its technological and methodological limitations. [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 | 0.055 | 0.326 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".