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251 Results In this study, we examined 17.9 million research articles in the Web of Science (WOS) to see how prior work is combined. We present facts that inform (i) the extent to which scientific papers reference novel versus conventional combinations of prior work, (ii) the relative impact of papers based on the combinations they draw upon, and (iii) how (i) and (ii) are associated with collaboration. We considered pairwise combinations of references in the bibliography of each paper (Small, 1973; Stringer et al., 2010). We counted the frequency of each co- citation pair across all papers published that year in the WOS and compared these observed frequencies to those expected by chance, using randomized citation net- works. In the randomized citation networks, all citation links between all papers in the WOS were switched using a Monte Carlo algorithm. The switching algorithm preserves the total citation counts to and from each paper, and the distribution of these citations counts forward and backward in time to ensure that a paper (or jour- nal) with n citations in the observed network will have n citations in the randomized network. For both the observed and the randomized paper-to-paper citation net- works, we aggregated counts of paper pairs into their respective journal pairs to focus on domain-level combinations (Itzkovitz et al., 2003; Stringer et al., 2008, 2010). In the data, there were over 122 million potential journal pairs created by the 15,613 journals indexed in the WOS. Comparing the observed frequency with the frequency distribution created with the randomized citation networks, we generated a z-score for each journal pair. This normalized measure describes whether any given pair appeared novel or conven- tional. Z-scores above zero indicate pairs that appeared more often in the observed data than expected by chance, indicating relatively common or “conventional” pair- ings. Z-scores below zero indicate pairs that appear less often in the observed WOS than expected by chance, indicating relatively atypical or “novel” pairings. For example, in the year 1995, the pairing Nature and Journal of Climate had a high z-score (47.344) indicating a conventional pairing, while Nature paired with Monthly Weather Review had a negative z-score (−24.554) indicating a pairing more unusual than chance. The above method assigns each paper a distribution of journal pair z-scores based on the paper’s reference list (Fig. 12.4a). To characterize a paper’s tendency to draw together conventional and novel combinations of prior work, we took two summary statistics. First, to characterize the central tendency of a paper’s combina- tions, we considered the paper’s median z-score. The median allowed us to charac- terize conventionality in the paper’s main mass of combinations. Second, we considered the paper’s 10th percentile z-score. The left tail allows us to characterize the paper’s more unusual journal combinations where novelty may reside. We found that papers typically relied on very high degrees of conventionality. Figure 12.4b presents the distribution of papers’ median z-scores for the WOS in the indicated decades. Considering that a z-score below zero represents a journal pair that occurs less often than expected by chance, the analysis of median z-scores sug- 12 How Atypical Combinations of Scientific Ideas Are Related to Impact:

zurĂŒck zum  Buch Knowledge and Networks"
Knowledge and Networks
Titel
Knowledge and Networks
Autoren
Johannes GlĂŒckler
Emmanuel Lazega
Ingmar Hammer
Verlag
Springer Open
Ort
Cham
Datum
2017
Sprache
deutsch
Lizenz
CC BY 4.0
ISBN
978-3-319-45023-0
Abmessungen
15.5 x 24.1 cm
Seiten
390
Schlagwörter
Human Geography, Innovation/Technology Management, Economic Geography, Knowledge, Discourse
Kategorie
Technik
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