Identifying “hot papers” and papers with “delayed recognition” in large-scale datasets by using dynamically normalized citation impact scores is a research paper published in Scientometrics (2018). On theSindex it has a DataRank of 1.2. It has been cited 26 times, with 26 citing works in its 1-hop citation network.
"Hot papers" (HPs) are papers which received a boost of citations shortly after publication. Papers with "delayed recognition" (DRs) received scarcely impact over a long time period, before a considerable citation boost started. DRs have attracted a lot of attention in scientometrics and beyond. Based on a comprehensive dataset with more than 5,000,000 papers published between 1980 and 1990, we identified HPs and DRs. In contrast to many other studies on DRs, which are based on raw citation counts, we calculated dynamically field-normalized impact scores for the search of HPs and DRs. This study is intended to investigate the differences between HPs (n = 323) and DRs (n = 315). The investigation of the journals which have published HPs and DRs revealed that some journals (e.g. Physical Review Letters and PNAS) were able to publish significantly more HPs than other journals. This pattern did not appear in DRs. Many HPs and DRs have been published by authors from the USA; however, in contrast to other countries, authors from the USA have published statistically significantly more HPs than DRs. Whereas "Biochemistry & Molecular Biology," "Immunology," and "Cell Biology" have published significantly more HPs than DRs, the opposite result arrived for "Surgery" and "Orthopedics." The results of the analysis of certain properties of HPs and DRs (e.g. number of pages) suggest that the emergence of DRs is an unpredictable process.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.494
From this paper's citation signal
Citation Network Contribution
0.660
From 20 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 43% comes from its base citations and 57% from the citation network (20 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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