Multiple Databases are Needed to Search the Journal Literature on Computer Science
Bibliographic record
Abstract
A Review of: Cavacini, A. (2015). What is the best database for computer science journal articles? Scientometrics 102(3): 2059-2071. http://dx.doi.org/10.1007/s11192-014-1506-1 Objective – To compare the coverage of computer science literature in four bibliographic databases by checking the indexing of a selection of journal articles. The purpose of this comparison was to identify the most comprehensive database in computer science and determine whether more than one database is needed to search for articles on computer science topics. Design – Comparative database evaluation using citation analysis. Setting – Computer science journal literature found within the INSPEC, Scopus, Web of Science, and DBLP databases. Subjects – 1,135 computer science journal articles published by an Italian university’s researchers from 1979 to 2014. Methods – The University of Milan’s institutional repository (AIR), containing publications authored by the university’s researchers, was searched in October 2014 for journal articles that were assigned the subject heading “informatica” (the word for computer science in Italian). The author then searched the titles of these journal articles in each of the databases to check whether they were indexed. For articles indexed in all four databases, the author also examined the quality of the bibliographic records by looking for the presence of 20 elements (e.g., the “cited by” option, ranking of search results, precision of results, etc.) in each database’s record. These overlapping articles were also searched in Google Scholar to help compare the quality of the records between the databases. Main Results – Scopus indexed 75.86% of the journal articles found in AIR, Web of Science indexed 64.49%, DBLP indexed 61.15%, and INSPEC indexed 53.39%. Web of Science and INSPEC put together covered 74.80% of the articles, which is comparable to the amount indexed by Scopus. DBLP and Scopus contained the highest number of references to articles that were not found in the other databases, about 4% each. Out of the 1,135 journal articles, 391 (34.45%) were indexed by all four databases, with Web of Science scoring the highest for providing the best quality bibliographic records for these articles. Conclusions – According to the author, the findings showed that INSPEC, Scopus, Web of Science, and DBLP “complemented each other, in a way that neither one could replace the other” (p. 2068) when searching the computer science literature. While there was overlap between databases, they each also contained unique articles.
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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 | 0.034 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.115 | 0.145 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.065 | 0.036 |
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".