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Trump cards: When Web of Science meets Harvard Law Review

Hui Li & Peicheng Wu

Journal of Information Science2026https://doi.org/10.1177/01655515261421186article
AJG 2
Weight
0.50

What the paper says

In the scientific community and among various evaluators, Web of Science is regarded as a leading and reliable data source. Given the distinctive features of legal publishing, the metadata quality of law journals in Web of Science may be seriously affected which hinders the reliability of literature retrieval, research evaluation and ranking. Focusing on this under-explored topic, this study identifies a significant proportion of metadata issues in the two ‘Trump Cards’ meet scenario. Specifically, the established Web of Science meets the leading journal Harvard Law Review, which contrasts with the low rates of metadata defects reported in previous studies. Four representative scenarios are identified: mis-indexing of non-Harvard Law Review records, omission of a large proportion of published records, inconsistent assignment of document types for similar content and inaccurate identification of authors’ names. This study not only highlights the serious metadata issues of a leading law review within the Web of Science for the scientific community, evaluators and database providers, but also probes the possible causes and potential impacts. Several recommendations are proposed to enhance the metadata quality of legal journals in Web of Science.

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https://doi.org/https://doi.org/10.1177/01655515261421186

Or copy a formatted citation

@article{hui2026,
  title        = {{Trump cards: When Web of Science meets Harvard Law Review}},
  author       = {Hui Li & Peicheng Wu},
  journal      = {Journal of Information Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/01655515261421186},
}

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Trump cards: When Web of Science meets Harvard Law Review

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.