Inter‐rater reliability of h‐index scores calculated by Web of Science and Scopus for clinical epidemiology scientists
Bibliographic record
Abstract
OBJECTIVE: We investigated the inter-rater reliability of Web of Science (WoS) and Scopus when calculating the h-index of 25 senior scientists in the Clinical Epidemiology Program of the Ottawa Hospital Research Institute. MATERIALS AND METHODS: Bibliometric information and the h-indices for the subjects were computed by four raters using the automatic calculators in WoS and Scopus. Correlation and agreement between ratings was assessed using Spearman's correlation coefficient and a Bland-Altman plot, respectively. RESULTS: Data could not be gathered from Google Scholar due to feasibility constraints. The Spearman's rank correlation between the h-index of scientists calculated with WoS was 0.81 (95% CI 0.72-0.92) and with Scopus was 0.95 (95% CI 0.92-0.99). The Bland-Altman plot showed no significant rater bias in WoS and Scopus; however, the agreement between ratings is higher in Scopus compared to WoS. CONCLUSION: Our results showed a stronger relationship and increased agreement between raters when calculating the h-index of a scientist using Scopus compared to WoS. The higher inter-rater reliability and simple user interface used in Scopus may render it the more effective database when calculating the h-index of senior scientists in epidemiology.
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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 | 0.213 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".