Search (2 results, page 1 of 1)

  • × author_ss:"Li, K."
  • × year_i:[2020 TO 2030}
  1. Wu, C.; Yan, E.; Zhu, Y.; Li, K.: Gender imbalance in the productivity of funded projects : a study of the outputs of National Institutes of Health R01 grants (2021) 0.00
    0.0025225044 = product of:
      0.03531506 = sum of:
        0.03531506 = weight(_text_:representation in 391) [ClassicSimilarity], result of:
          0.03531506 = score(doc=391,freq=2.0), product of:
            0.11578492 = queryWeight, product of:
              4.600994 = idf(docFreq=1206, maxDocs=44218)
              0.025165197 = queryNorm
            0.3050057 = fieldWeight in 391, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.600994 = idf(docFreq=1206, maxDocs=44218)
              0.046875 = fieldNorm(doc=391)
      0.071428575 = coord(1/14)
    
    Abstract
    This study examines the relationship between team's gender composition and outputs of funded projects using a large data set of National Institutes of Health (NIH) R01 grants and their associated publications between 1990 and 2017. This study finds that while the women investigators' presence in NIH grants is generally low, higher women investigator presence is on average related to slightly lower number of publications. This study finds empirically that women investigators elect to work in fields in which fewer publications per million-dollar funding is the norm. For fields where women investigators are relatively well represented, they are as productive as men. The overall lower productivity of women investigators may be attributed to the low representation of women in high productivity fields dominated by men investigators. The findings shed light on possible reasons for gender disparity in grant productivity.
  2. Li, K.; Jiao, C.: ¬The data paper as a sociolinguistic epistemic object : a content analysis on the rhetorical moves used in data paper abstracts (2022) 0.00
    0.0021020873 = product of:
      0.02942922 = sum of:
        0.02942922 = weight(_text_:representation in 560) [ClassicSimilarity], result of:
          0.02942922 = score(doc=560,freq=2.0), product of:
            0.11578492 = queryWeight, product of:
              4.600994 = idf(docFreq=1206, maxDocs=44218)
              0.025165197 = queryNorm
            0.25417143 = fieldWeight in 560, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.600994 = idf(docFreq=1206, maxDocs=44218)
              0.0390625 = fieldNorm(doc=560)
      0.071428575 = coord(1/14)
    
    Abstract
    The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves-rhetorical units performing a coherent narrative function-are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well-received system that focuses on English-language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of introduction, method, results, and discussion- and data-oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data-oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system.

Authors