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To overcome these constraints and enable the early forecasting of high-impact outcomes, the field has evolved toward incorporating alternative signals and advanced modeling techniques. This review traces the evolutionary trajectory of high-impact prediction research. We first examine the foundational role of citation analysis as both a target definition and a baseline heuristic. We then explore how researchers have integrated early-available alternative sources (e.g., journal attributes, author reputation, and content signals) to bridge the gap before citations accumulate. Furthermore, we analyze the progression of methodological approaches, ranging from network analysis and regression models to the recent advances in machine learning. By distinguishing between identification and prediction tasks, this review provides a comprehensive historical synthesis of how the field has advanced from simple metrics to complex predictive systems.<\/jats:p>","DOI":"10.1007\/s11192-026-05660-8","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:59:55Z","timestamp":1780037995000},"page":"4001-4047","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of high-impact research: a historical review and research opportunities"],"prefix":"10.1007","volume":"131","author":[{"given":"Gangmin","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4969-3912","authenticated-orcid":false,"given":"Sangyoon","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"issue":"1","key":"5660_CR1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.joi.2018.11.003","volume":"13","author":"G Abramo","year":"2019","unstructured":"Abramo, G., D\u2019Angelo, C. 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