Search (6 results, page 1 of 1)

0.12668116 = product of:
  0.25336233 = sum of:
    0.25336233 = sum of:
      0.2121643 = weight(_text_:maps in 2736) [ClassicSimilarity], result of:
        0.2121643 = score(doc=2736,freq=8.0), product of:
          0.28477904 = queryWeight, product of:
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.050679237 = queryNorm
          0.7450137 = fieldWeight in 2736, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            5.619245 = idf(docFreq=435, maxDocs=44218)
            0.046875 = fieldNorm(doc=2736)
      0.041198023 = weight(_text_:22 in 2736) [ClassicSimilarity], result of:
        0.041198023 = score(doc=2736,freq=2.0), product of:
          0.17747006 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.050679237 = queryNorm
          0.23214069 = fieldWeight in 2736, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=2736)
  0.5 = coord(1/2)

Abstract

A consensus map of science is generated from an analysis of 20 existing maps of science. These 20 maps occur in three basic forms: hierarchical, centric, and noncentric (or circular). The consensus map, generated from consensus edges that occur in at least half of the input maps, emerges in a circular form. The ordering of areas is as follows: mathematics is (arbitrarily) placed at the top of the circle, and is followed clockwise by physics, physical chemistry, engineering, chemistry, earth sciences, biology, biochemistry, infectious diseases, medicine, health services, brain research, psychology, humanities, social sciences, and computer science. The link between computer science and mathematics completes the circle. If the lowest weighted edges are pruned from this consensus circular map, a hierarchical map stretching from mathematics to social sciences results. The circular map of science is found to have a high level of correspondence with the 20 existing maps, and has a variety of advantages over hierarchical and centric forms. A one-dimensional Riemannian version of the consensus map is also proposed.

Date

22. 3.2009 12:49:33

0.054134816 = product of:
  0.10826963 = sum of:
    0.10826963 = product of:
      0.21653926 = sum of:
        0.21653926 = weight(_text_:maps in 4956) [ClassicSimilarity], result of:
          0.21653926 = score(doc=4956,freq=12.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.7603764 = fieldWeight in 4956, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4956)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We describe two general approaches to creating document-level maps of science. To create a local map, one defines and directly maps a sample of data, such as all literature published in a set of information science journals. To create a global map of a research field, one maps "all of science" and then locates a literature sample within that full context. We provide a deductive argument that global mapping should create more accurate partitions of a research field than does local mapping, followed by practical reasons why this may not be so. The field of information science is then mapped at the document level using both local and global methods to provide a case illustration of the differences between the methods. Textual coherence is used to assess the accuracies of both maps. We find that document clusters in the global map have significantly higher coherence than do those in the local map, and that the global map provides unique insights into the field of information science that cannot be discerned from the local map. Specifically, we show that information science and computer science have a large interface and that computer science is the more progressive discipline at that interface. We also show that research communities in temporally linked threads have a much higher coherence than do isolated communities, and that this feature can be used to predict which threads will persist into a subsequent year. Methods that could increase the accuracy of both local and global maps in the future also are discussed.

0.0375057 = product of:
  0.0750114 = sum of:
    0.0750114 = product of:
      0.1500228 = sum of:
        0.1500228 = weight(_text_:maps in 1230) [ClassicSimilarity], result of:
          0.1500228 = score(doc=1230,freq=4.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.5268042 = fieldWeight in 1230, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=1230)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The majority of the effort in metrics research has addressed research evaluation. Far less research has addressed the unique problems of research planning. Models and maps of science that can address the detailed problems associated with research planning are needed. This article reports on the creation of an article-level model and map of science covering 16 years and nearly 20 million articles using cocitation-based techniques. The map is then used to define discipline-like structures consisting of natural groupings of articles and clusters of articles. This combination of detail and high-level structure can be used to address planning-related problems such as identification of emerging topics and the identification of which areas of science and technology are innovative and which are simply persisting. In addition to presenting the model and map, several process improvements that result in greater accuracy structures are detailed, including a bibliographic coupling approach for assigning current papers to cocitation clusters and a sequential hybrid approach to producing visual maps from models.

0.03125475 = product of:
  0.0625095 = sum of:
    0.0625095 = product of:
      0.125019 = sum of:
        0.125019 = weight(_text_:maps in 5252) [ClassicSimilarity], result of:
          0.125019 = score(doc=5252,freq=4.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.43900353 = fieldWeight in 5252, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5252)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written an how to quantitatively evaluate the accuracy of relatedness measures or the resulting maps. The authors propose a new framework for assessing the performance of relatedness measures and visualization algorithms that contains four factors: accuracy, coverage, scalability, and robustness. This method was applied to 10 measures of journal-journal relatedness to determine the best measure. The 10 relatedness measures were then used as inputs to a visualization algorithm to create an additional 10 measures of journal-journal relatedness based an the distances between pairs of journals in two-dimensional space. This second step determines robustness (i.e., which measure remains best after dimension reduction). Results show that, for low coverage (under 50%), the Pearson correlation is the most accurate raw relatedness measure. However, the best overall measure, both at high coverage, and after dimension reduction, is the cosine index or a modified cosine index. Results also showed that the visualization algorithm increased local accuracy for most measures. Possible reasons for this counterintuitive finding are discussed.

0.026520537 = product of:
  0.053041074 = sum of:
    0.053041074 = product of:
      0.10608215 = sum of:
        0.10608215 = weight(_text_:maps in 4214) [ClassicSimilarity], result of:
          0.10608215 = score(doc=4214,freq=2.0), product of:
            0.28477904 = queryWeight, product of:
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.050679237 = queryNorm
            0.37250686 = fieldWeight in 4214, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.619245 = idf(docFreq=435, maxDocs=44218)
              0.046875 = fieldNorm(doc=4214)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We investigated the similarities of pairs of articles that are cocited at the different cocitation levels of the journal, article, section, paragraph, sentence, and bracket. Our results indicate that textual similarity, intellectual overlap (shared references), author overlap (shared authors), proximity in publication time all rise monotonically as the cocitation level gets lower (from journal to bracket). While the main gain in similarity happens when moving from journal to article cocitation, all level changes entail an increase in similarity, especially section to paragraph and paragraph to sentence/bracket levels. We compared the results from four journals over the years 2010-2015: Cell, the European Journal of Operational Research, Physics Letters B, and Research Policy, with consistent general outcomes and some interesting differences. Our findings motivate the use of granular cocitation information as defined by meaningful units of text, with implications for, among others, the elaboration of maps of science and the retrieval of scholarly literature.

0.024276167 = product of:
  0.048552334 = sum of:
    0.048552334 = product of:
      0.09710467 = sum of:
        0.09710467 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.09710467 = score(doc=1352,freq=4.0), product of:
            0.17747006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050679237 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40