research library managers

Abstract: Selective databases such as Scopus periodically re-evaluate the titles they index and remove those that no longer meet their quality criteria. Once a journal is removed, its subsequent output is no longer recorded, and the consequences of this process have therefore received little attention. This study analyzes the publication output and citation impact of 514 sources delisted from Scopus between 2018 and 2024, based on 678,162 documents retrieved from OpenAlex over a window of three years before and after delisting. Differences between periods are tested with the Wilcoxon signed-rank test for paired samples, globally and by geographic region, field of knowledge and SJR quartile. Output grows until the year of delisting and falls sharply afterwards. The median annual output per journal drops from 35 to 22 documents (-37.1%; p < 0.001; r = 0.30), 61.7% of journals publish less, and 84 (16.3%) have no documents recorded in OpenAlex after delisting. Citations per document, measured over a three-year window, barely change when each journal is compared with itself (-2.4%; p = 0.079), and journals are almost evenly split between those whose impact falls and those whose impact holds steady or rises. The results indicate that delisting makes these journals less attractive as publication venues but does not penalize the citation of the work they publish to the same extent, which raises concerns from a research integrity perspective.

Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a paper's contribution. We introduce contribution-based credit scoring for research articles: a structured citation analysis that decomposes a paper's credit between its own original contribution and the prior work it builds on. Motivated by a cooperative-game view of scientific credit, we propose the contribution tree, a hierarchical framework that conserves importance across the document structure and separates original from citation-derived contribution. To make this framework scalable, we use LLMs as noisy comparative estimators of local importance. We further extend the model to article collections by propagating contributions through weighted citation graphs, yielding corpus-level contributions and normalized influence scores. Our experiments suggest that our framework captures contribution signals beyond surface-level heuristics. Our code is available at https://github.com/sanaebrahimi/Importance_Scoring/