Examining the quality of the corresponding authorship field in Web of Science and Scopus
Bibliographic record
Abstract
Abstract Authorship is associated with scientific capital and prestige, and corresponding authorship is used in evaluation as a proxy for scientific status. However, there are no empirical analyses on the validity of the corresponding authorship metadata in bibliometric databases. This paper looks at differences in the corresponding authorship metadata in Web of Science (WoS) and Scopus to investigate how the relationship between author position and corresponding authors varies by discipline and country and analyzes changes in the position of corresponding authors over time. We find that both WoS and Scopus have accuracy issues when it comes to assigning corresponding authorship. Although the number of documents with a reprint author has increased over time in both databases, WoS indexed more of those papers than Scopus, and there are significant differences between the two databases in terms of who the corresponding author is. Although metadata is not complete in WoS, corresponding authors are normally first authors with a declining trend over time, favoring middle and last authors, especially in the Medical, Natural Sciences, and Engineering fields. These results reinforce the importance of considering how databases operationalize and index concepts such as corresponding authors, this being particularly important when they are used in research assessment.
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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 | 0.023 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.076 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".