Pandemics are catalysts of scientific novelty: Evidence from <scp>COVID</scp>‐19

Liu, Meijun; Bu, Yi; Chen, Chongyan; Xu, Jian; Li, Daifeng; Leng, Yan; Freeman, Richard B.; Meyer, Eric T.; Yoon, Wonjin; Sung, Mujeen; Jeong, Minbyul; Lee, Jinhyuk; Kang, Jaewoo; Min, Chao; Song, Min; Zhai, Yujia; Ding, Ying

doi:10.1002/asi.24612

Cited by 31 publications

(17 citation statements)

References 79 publications

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“…When excluding international collaborations and productive features, we found that the performance decreased (Recall = 0.63, Precision = 0.37, AUC = 0.61), which means the two author-related features are important. Although a research investigated Scopus data of 2005 found that international collaboration tends to produce conventional research (Wagner et al, 2019 ), a recently study focused on COVID-19 field reached the opposite conclusion (Liu et al, 2021 ). Our study of materials science supported, to some extent, the later opinion, for the reason that this filed is labor intensive and device induced.…”

Section: Resultsmentioning

confidence: 99%

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

2022

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The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

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Section: Resultsmentioning

confidence: 99%

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

2022

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“…This approach has been leveraged in previous work on the analysis of patent novelty, where document embedding models have been used to generate feature vector representations for patents, allowing the cosine similarity between them to be calculated [20]. There are also domain specific models that can be used for document embeddings, such as BioBERT, which as has similarly been used to assess the relative novelty of PubMed Articles by generating document feature vectors [37].…”

Section: Related Literature 21 Novelty Metricsmentioning

confidence: 99%

Novelty and cultural evolution in modern popular music

O'Toole

Horvát

2023

EPJ Data Sci.

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The ubiquity of digital music consumption has made it possible to extract information about modern music that allows us to perform large scale analysis of stylistic change over time. In order to uncover underlying patterns in cultural evolution, we examine the relationship between the established characteristics of different genres and styles, and the introduction of novel ideas that fuel this ongoing creative evolution. To understand how this dynamic plays out and shapes the cultural ecosystem, we compare musical artifacts to their contemporaries to identify novel artifacts, study the relationship between novelty and commercial success, and connect this to the changes in musical content that we can observe over time. Using Music Information Retrieval (MIR) data and lyrics from Billboard Hot 100 songs between 1974-2013, we calculate a novelty score for each song’s aural attributes and lyrics. Comparing both scores to the popularity of the song following its release, we uncover key patterns in the relationship between novelty and audience reception. Additionally, we look at the link between novelty and the likelihood that a song was influential given where its MIR and lyrical features fit within the larger trends we observed.

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“…Accordingly, researchers are likely to prefer "known collaborators" (Fry et al, 2020), which could disadvantage those outside the network. Nevertheless, the pandemic motivated researchers to collaborate with those with whom they had not previously worked, with such first-time international collaboration positively associated with the novelty of research (Liu et al, 2021).…”

Section: Equitable Opportunities For Virtual Academic Mobility In Asiamentioning

confidence: 99%

Mobility for Academic Collaboration Post-COVID-19

Oanda

Jon

Blanco

2022

IJAHE

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In-person mobility has traditionally been taken for granted as an element of academic collaboration. The COVID-19 pandemic disrupted this status quo, introducing new challenges, especially across Africa, Asia and Latin America, where local economies and higher education systems have been disproportionately affected, exacerbating existing inequities. Low and unequal vaccination rates in these regions will likely continue to influence academic mobility. Given that international travel is set to remain complicated and expensive, African, Asian and Latin American academics’ preference for North America and Europe as destinations for mobility is likely to shift, with new academic mobility ecosystems emerging. Indeed, strong institutions and countries in these regions are becoming new hubs for intra-regional mobility and collaboration. The future of academic mobility and collaboration in Africa, Asia and Latin America is thus likely to include alternative destinations and virtual mobility, with the possibility of lower levels of international cooperation as the perceived value of mobility comes into question. These changes call for creative, long-term plans by institutions as well as governments. They present opportunities to promote mobility within regions, as well as South-South mobility in order to increase higher education’s social relevance.

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Pandemics are catalysts of scientific novelty: Evidence from COVID‐19

Cited by 31 publications

References 79 publications

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

Novelty and cultural evolution in modern popular music

Mobility for Academic Collaboration Post-COVID-19

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