The Usefulness of Related Functions in Web of Science and Scopus
Bibliographic record
Abstract
Abstract Objective: This study evaluates the effectiveness of the related search functions in Web of Science and Scopus. Web of Science has one related search function (searching by references) whereas Scopus has three related search functions (searching by references, authors, or keywords). Methods: Thirty queries were searched in both Web of Science and Scopus. For each query, the most relevant document was retrieved and its first thirty related documents were assessed for relevancy to the original query. Results for both databases were compared using the median values of precision. For Scopus the three different methods of relevance were compared using median precision values. Results: The median precision value for the related documents retrieved from Web of Science was 0.63, while the median for those retrieved from Scopus using the related by references function was 0.62. A Wilcoxon test showed no significant difference in the two medians. In the comparison of the three related functions in Scopus, the median precision values were 0.62, 0.42, and 0.43 for the related search functions by references, authors, and keywords respectively. A Friedman's test showed that the median precision value for relatedness by references was significantly higher than the median vales for the other two related functions. In Scopus, the effectiveness of the related search function using all keywords was not as effective when compared to the effectiveness using select keywords. The median precision value with select keywords was 0.17. Conclusions: The related search functions by references for both Web of Science and Scopus were moderately effective in retrieving additional relevant documents on a given topic, and there was no significant difference in their performance. When comparing the three methods available in Scopus, the related search function by references was found to be more effective than the system's related functions by authors and keywords.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.201 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".