Search (4 results, page 1 of 1)

0.045544453 = product of:
  0.12752447 = sum of:
    0.021217827 = weight(_text_:subject in 3095) [ClassicSimilarity], result of:
      0.021217827 = score(doc=3095,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.19758089 = fieldWeight in 3095, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3095)
    0.023791125 = weight(_text_:classification in 3095) [ClassicSimilarity], result of:
      0.023791125 = score(doc=3095,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 3095, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3095)
    0.033585876 = product of:
      0.06717175 = sum of:
        0.06717175 = weight(_text_:schemes in 3095) [ClassicSimilarity], result of:
          0.06717175 = score(doc=3095,freq=4.0), product of:
            0.16067243 = queryWeight, product of:
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.03002521 = queryNorm
            0.41806644 = fieldWeight in 3095, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.3512506 = idf(docFreq=569, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3095)
      0.5 = coord(1/2)
    0.02513852 = weight(_text_:bibliographic in 3095) [ClassicSimilarity], result of:
      0.02513852 = score(doc=3095,freq=2.0), product of:
        0.11688946 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03002521 = queryNorm
        0.21506234 = fieldWeight in 3095, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3095)
    0.023791125 = weight(_text_:classification in 3095) [ClassicSimilarity], result of:
      0.023791125 = score(doc=3095,freq=4.0), product of:
        0.09562149 = queryWeight, product of:
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.03002521 = queryNorm
        0.24880521 = fieldWeight in 3095, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1847067 = idf(docFreq=4974, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3095)
  0.35714287 = coord(5/14)

Abstract

The aggregated journal-journal citation matrix - based on the Journal Citation Reports (JCR) of the Science Citation Index - can be decomposed by indexers or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel and Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.

0.003388893 = product of:
  0.0474445 = sum of:
    0.0474445 = weight(_text_:subject in 2713) [ClassicSimilarity], result of:
      0.0474445 = score(doc=2713,freq=10.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.4418043 = fieldWeight in 2713, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2713)
  0.071428575 = coord(1/14)

Abstract

The decomposition of scientific literature into disciplinary and subdisciplinary structures is one of the core goals of scientometrics. How can we achieve a good decomposition? The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical matrix (citing versus cited) that is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis of the matrix of subject categories suggests a 14-factor solution. This solution could be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are online at http://www.leydesdorff.net/map06. Presumably, inaccuracies in the attribution of journals to the ISI subject categories average out so that the factor analysis reveals the main structures. The mapping of science could, therefore, be comprehensive and reliable on a large scale albeit imprecise in terms of the attribution of journals to the ISI subject categories.

0.0025719889 = product of:
  0.036007844 = sum of:
    0.036007844 = weight(_text_:subject in 494) [ClassicSimilarity], result of:
      0.036007844 = score(doc=494,freq=4.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.33530587 = fieldWeight in 494, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=494)
  0.071428575 = coord(1/14)

Abstract

Multiple perspectives on the nonlinear processes of medical innovations can be distinguished and combined using the Medical Subject Headings (MeSH) of the MEDLINE database. Focusing on three main branches-"diseases," "drugs and chemicals," and "techniques and equipment"-we use base maps and overlay techniques to investigate the translations and interactions and thus to gain a bibliometric perspective on the dynamics of medical innovations. To this end, we first analyze the MEDLINE database, the MeSH index tree, and the various options for a static mapping from different perspectives and at different levels of aggregation. Following a specific innovation (RNA interference) over time, the notion of a trajectory which leaves a signature in the database is elaborated. Can the detailed index terms describing the dynamics of research be used to predict the diffusion dynamics of research results? Possibilities are specified for further integration between the MEDLINE database on one hand, and the Science Citation Index and Scopus (containing citation information) on the other.

0.0018186709 = product of:
  0.02546139 = sum of:
    0.02546139 = weight(_text_:subject in 4445) [ClassicSimilarity], result of:
      0.02546139 = score(doc=4445,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.23709705 = fieldWeight in 4445, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.046875 = fieldNorm(doc=4445)
  0.071428575 = coord(1/14)

Abstract

Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nanocrystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI subject categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time while the network evolves into an oligopolistic control structure with small-world characteristics. The transition from disciplinary-oriented ("Mode 1") to transfer-oriented ("Mode 2") research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC.