Search (316 results, page 15 of 16)

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 1357) [ClassicSimilarity], result of:
          0.025961377 = score(doc=1357,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 1357, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1357)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Despite a very large number of studies on the aging and obsolescence of scientific literature, no study has yet measured, over a very long time period, the changes in the rates at which scientific literature becomes obsolete. This article studies the evolution of the aging phenomenon and, in particular, how the age of cited literature has changed over more than 100 years of scientific activity. It shows that the average and median ages of cited literature have undergone several changes over the period. Specifically, both World War I and World War II had the effect of significantly increasing the age of the cited literature. The major finding of this article is that contrary to a widely held belief, the age of cited material has risen continuously since the mid-1960s. In other words, during that period, researchers were relying on an increasingly old body of literature. Our data suggest that this phenomenon is a direct response to the steady-state dynamics of modern science that followed its exponential growth; however, we also have observed that online preprint archives such as arXiv have had the opposite effect in some subfields.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 2713) [ClassicSimilarity], result of:
          0.025961377 = score(doc=2713,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 2713, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2713)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

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.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 3701) [ClassicSimilarity], result of:
          0.025961377 = score(doc=3701,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 3701, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3701)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition; however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 4347) [ClassicSimilarity], result of:
          0.025961377 = score(doc=4347,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 4347, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4347)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

This study investigated name changes of women authors to determine how they were represented in indexes and cited references and identify problem areas. A secondary purpose of the study was to investigate whether or not indexing services were using authority control and how this influenced the search results. The works of eight library science authors who had published under multiple names were examined. The researchers compared author names as they appeared on title pages of publications versus in four online databases and in bibliographies by checking 380 publications and 1,159 citations. Author names were correctly provided 81.22% of the time in indexing services and 90.94% in citation lists. The lowest accuracy (54.55%) occurred when limiting to publications found in Library Literature. The highest accuracy (94.18%) occurred with works published before a surname changed. Author names in indexes and citations correctly matched names on journal articles more often than for any other type of publication. Indexes and citation style manuals treated author names in multiple ways, often altering names substantially from how they appear on the title page. Recommendations are made for changes in editorial styles by indexing services and by the authors themselves to help alleviate future confusion in author name searching.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 1814) [ClassicSimilarity], result of:
          0.025961377 = score(doc=1814,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 1814, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1814)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals?=?20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web of Science (N?=?10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 3760) [ClassicSimilarity], result of:
          0.025961377 = score(doc=3760,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 3760, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3760)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Although news stories target the general public and are sometimes inaccurate, they can serve as sources of real-world information for researchers. This article investigates the extent to which academics exploit journalism using content and citation analyses of online BBC News stories cited by Scopus articles. A total of 27,234 Scopus-indexed publications have cited at least one BBC News story, with a steady annual increase. Citations from the arts and humanities (2.8% of publications in 2015) and social sciences (1.5%) were more likely than citations from medicine (0.1%) and science (<0.1%). Surprisingly, half of the sampled Scopus-cited science and technology (53%) and medicine and health (47%) stories were based on academic research, rather than otherwise unpublished information, suggesting that researchers have chosen a lower-quality secondary source for their citations. Nevertheless, the BBC News stories that were most frequently cited by Scopus, Google Books, and Wikipedia introduced new information from many different topics, including politics, business, economics, statistics, and reports about events. Thus, news stories are mediating real-world knowledge into the academic domain, a potential cause for concern.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 5328) [ClassicSimilarity], result of:
          0.025961377 = score(doc=5328,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 5328, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5328)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

As the online dissemination of scholarly outputs gets faster and easier, altmetrics, social media based indices, have emerged alongside traditional metrics for research evaluation. In a two-phase survey, we investigate scholars' familiarity and usage of traditional metrics and altmetrics. In this paper, we present the second phase with 448 participants. We found few traditional metrics, like the Journal Impact Factor and number of citations, are familiar to and often used by scholars for research evaluation. Among altmetrics, only views/downloads, readers, and followers are known to more than half the respondents. Unseen benefits and lack of time are hindrances to using metrics for the evaluation of research outputs. Although social media are well-known, scholars prefer promoting their research by publishing in journals and attending conferences. We found social media usage, perceived ease of use and usefulness of altmetrics affect the usage of altmetrics. Findings suggest altmetrics have attracted attention in academia and could be considered complementary to traditional metrics. We acknowledge that due to the limited sample size, statistics and demographics in this study, findings cannot be said to be representative of the entire academic population worldwide. Future studies are needed that cover a wider range of academic disciplines around the world.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 5969) [ClassicSimilarity], result of:
          0.025961377 = score(doc=5969,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 5969, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5969)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

There is currently a paucity of evidence-based strategies that have been shown to increase citations of peer-reviewed articles following their publication. We conducted a 12-month randomized controlled trial to examine whether the promotion of article links in an online cross-publisher distribution platform (TrendMD) affects citations. In all, 3,200 articles published in 64 peer-reviewed journals across eight subject areas were block randomized at the subject level to either the TrendMD group (n = 1,600) or the control group (n = 1,600) of the study. Our primary outcome compares the mean citations of articles randomized to TrendMD versus control after 12 months. Articles randomized to TrendMD showed a 50% increase in mean citations relative to control at 12 months. The difference in mean citations at 12 months for articles randomized to TrendMD versus control was 5.06, 95% confidence interval [2.87, 7.25], was statistically significant (p?<?.001) and found in three of eight subject areas. At 6 months following publication, articles randomized to TrendMD showed a smaller, yet statistically significant (p = .005), 21% increase in mean citations, relative to control. To our knowledge, this is the first randomized controlled trial to demonstrate how an intervention can be used to increase citations of peer-reviewed articles after they have been published.

0.0028845975 = product of:
  0.008653793 = sum of:
    0.008653793 = product of:
      0.025961377 = sum of:
        0.025961377 = weight(_text_:online in 247) [ClassicSimilarity], result of:
          0.025961377 = score(doc=247,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 247, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=247)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The amount of annually published scholarly articles is growing steadily, as is the number of indicators through which impact of publications is measured. Little is known about how the increasing variety of available metrics affects researchers' processes of selecting literature to read. We conducted ranking experiments embedded into an online survey with 247 participating researchers, most from social sciences. Participants completed series of tasks in which they were asked to rank fictitious publications regarding their expected relevance, based on their scores regarding six prototypical metrics. Through applying logistic regression, cluster analysis, and manual coding of survey answers, we obtained detailed data on how prominent metrics for research impact influence our participants in decisions about which scientific articles to read. Survey answers revealed a combination of qualitative and quantitative characteristics that researchers consult when selecting literature, while regression analysis showed that among quantitative metrics, citation counts tend to be of highest concern, followed by Journal Impact Factors. Our results suggest a comparatively favorable view of many researchers on bibliometrics and widespread skepticism toward altmetrics. The findings underline the importance of equipping researchers with solid knowledge about specific metrics' limitations, as they seem to play significant roles in researchers' everyday relevance assessments.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 5767) [ClassicSimilarity], result of:
          0.025790809 = score(doc=5767,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 5767, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5767)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

We explore the intellectual subject structure and research themes in software engineering through the identification and analysis of a core journal literature. We examine this literature via two expert perspectives: that of the author, who identified significant work by citing it (journal cocitation analysis), and that of the professional indexer, who tags published work with subject terms to facilitate retrieval from a bibliographic database (subject profile analysis). The data sources are SCISEARCH (the on-line version of Science Citation Index), and INSPEC (a database covering software engineering, computer science, and information systems). We use data visualization tools (cluster analysis, multidimensional scaling, and PFNets) to show the "intellectual maps" of software engineering. Cocitation and subject profile analyses demonstrate that software engineering is a distinct interdisciplinary field, valuing practical and applied aspects, and spanning a subject continuum from "programming-in-the-smalI" to "programming-in-the-large." This continuum mirrors the software development life cycle by taking the operating system or major application from initial programming through project management, implementation, and maintenance. Object orientation is an integral but distinct subject area in software engineering. Key differences are the importance of management and programming: (1) cocitation analysis emphasizes project management and systems development; (2) programming techniques/languages are more influential in subject profiles; (3) cocitation profiles place object-oriented journals separately and centrally while the subject profile analysis locates these journals with the programming/languages group

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 6667) [ClassicSimilarity], result of:
          0.025790809 = score(doc=6667,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 6667, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6667)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

How knowledge is organized in human memory is of interest to both information science and cognitive science. The current information retrieval (IR) systems can be improved if we understand which conceptual structures could facilitate users in information processing and seeking. This project examined twenty-two cognitive maps on ten research topics generated by ten experts and eleven non-experts. Experts were those who had completed a research project on the topic prior to participating in this study, while non-experts were from the same academic department who were familiar with the topic but had not conducted any in-depth research on it. A research topic can be represented by a vocabulary and the relationships among the terms in the vocabulary. A cognitive map visualizes the vocabulary and its configuration in a plane. We observed that experts did not generate the maps much faster than non-experts. Both experts and non-experts modified the given vocabulary by either adding or dropping terms. The dominant configuration for the maps was top-down, while five maps were orientated in left-right or radical structure (from a center). Experts tended to use problem-oriented approach to organize the vocabulary while non-experts often applied discipline-oriented hierarchical structure. Despite of many differences in vocabulary and structure by individuals, there are terms clustered in a similar ways across maps indicating an agreed-upon semantic closeness among these terms

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 1459) [ClassicSimilarity], result of:
          0.025790809 = score(doc=1459,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 1459, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1459)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

In their 1998 article "Visualizing a discipline: An author cocitation analysis of information science, 1972-1995," White and McCain used multidimensional scaling, hierarchical clustering, and factor analysis to display the specialty groupings of 120 highly-cited ("paradigmatic") information scientists. These statistical techniques are traditional in author cocitation analysis (ACA). It is shown here that a newer technique, Pathfinder Networks (PFNETs), has considerable advantages for ACA. In PFNETs, nodes represent authors, and explicit links represent weighted paths between nodes, the weights in this case being cocitation counts. The links can be drawn to exclude all but the single highest counts for author pairs, which reduces a network of authors to only the most salient relationships. When these are mapped, dominant authors can be defined as those with relatively many links to other authors (i.e., high degree centrality). Links between authors and dominant authors define specialties, and links between dominant authors connect specialties into a discipline. Maps are made with one rather than several computer routines and in one rather than many computer passes. Also, PFNETs can, and should, be generated from matrices of raw counts rather than Pearson correlations, which removes a computational step associated with traditional ACA. White and McCain's raw data from 1998 are remapped as a PFNET. It is shown that the specialty groupings correspond closely to those seen in the factor analysis of the 1998 article. Because PFNETs are fast to compute, they are used in AuthorLink, a new Web-based system that creates live interfaces for cocited author retrieval an the fly.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 1608) [ClassicSimilarity], result of:
          0.025790809 = score(doc=1608,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 1608, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1608)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Type/Token-Taken informetrics is a new part of informetrics that studies the use of items rather than the items itself. Here, items are the objects that are produced by the sources (e.g., journals producing articles, authors producing papers, etc.). In linguistics a source is also called a type (e.g., a word), and an item a token (e.g., the use of words in texts). In informetrics, types that occur often, for example, in a database will also be requested often, for example, in information retrieval. The relative use of these occurrences will be higher than their relative occurrences itself; hence, the name Type/ Token-Taken informetrics. This article studies the frequency distribution of Type/Token-Taken informetrics, starting from the one of Type/Token informetrics (i.e., source-item relationships). We are also studying the average number my* of item uses in Type/Token-Taken informetrics and compare this with the classical average number my in Type/Token informetrics. We show that my* >= my always, and that my* is an increasing function of my. A method is presented to actually calculate my* from my, and a given a, which is the exponent in Lotka's frequency distribution of Type/Token informetrics. We leave open the problem of developing non-Lotkaian Type/TokenTaken informetrics.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 3091) [ClassicSimilarity], result of:
          0.025790809 = score(doc=3091,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 3091, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3091)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Because of the increasing presence of scientific publications an the Web, combined with the existing difficulties in easily verifying and retrieving these publications, research an techniques and methods for retrieval of scientific Web publications is called for. In this article, we report an the initial steps taken toward the construction of a test collection of scientific Web publications within the subject domain of plant biology. The steps reported are those of data gathering and data analysis aiming at identifying characteristics of scientific Web publications. The data used in this article were generated based an specifically selected domain topics that are searched for in three publicly accessible search engines (Google, AlITheWeb, and AItaVista). A sample of the retrieved hits was analyzed with regard to how various publication attributes correlated with the scientific quality of the content and whether this information could be employed to harvest, filter, and rank Web publications. The attributes analyzed were inlinks, outlinks, bibliographic references, file format, language, search engine overlap, structural position (according to site structure), and the occurrence of various types of metadata. As could be expected, the ranked output differs between the three search engines. Apparently, this is caused by differences in ranking algorithms rather than the databases themselves. In fact, because scientific Web content in this subject domain receives few inlinks, both AItaVista and AlITheWeb retrieved a higher degree of accessible scientific content than Google. Because of the search engine cutoffs of accessible URLs, the feasibility of using search engine output for Web content analysis is also discussed.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 3092) [ClassicSimilarity], result of:
          0.025790809 = score(doc=3092,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 3092, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3092)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Although time has been recognized as an important dimension in the co-citation literature, to date it has not been incorporated into the analogous process of link analysis an the Web. In this paper, we discuss several aspects and uses of the time dimension in the context of Web information retrieval. We describe the ideal casewhere search engines track and store temporal data for each of the pages in their repository, assigning timestamps to the hyperlinks embedded within the pages. We introduce several applications which benefit from the availability of such timestamps. To demonstrate our claims, we use a somewhat simplistic approach, which dates links by approximating the age of the page's content. We show that by using this crude measure alone it is possible to detect and expose significant events and trends. We predict that by using more robust methods for tracking modifications in the content of pages, search engines will be able to provide results that are more timely and better reflect current real-life trends than those they provide today.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 6008) [ClassicSimilarity], result of:
          0.025790809 = score(doc=6008,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 6008, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6008)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Author Cocitation Analysis (ACA) and Web Colink Analysis (WCA) are examined as sister techniques in the related fields of bibliometrics and webometrics. Comparisons are made between the two techniques based on their data retrieval, mapping, and interpretation procedures, using mathematics as the subject in focus. An ACA is carried out and interpreted for a group of participants (authors) involved in an Isaac Newton Institute (2000) workshop-Singularity Theory and Its Applications to Wave Propagation Theory and Dynamical Systems-and compared/contrasted with a WCA for a list of international mathematics research institute home pages on the Web. Although the practice of ACA may be used to inform a WCA, the two techniques do not share many elements in common. The most important departure between ACA and WCA exists at the interpretive stage when ACA maps become meaningful in light of citation theory, and WCA maps require interpretation based on hyperlink theory. Much of the research concerning link theory and motivations for linking is still new; therefore further studies based on colinking are needed, mainly map-based studies, to understand what makes a Web colink structure meaningful.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 271) [ClassicSimilarity], result of:
          0.025790809 = score(doc=271,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 271, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=271)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Herdan's law in linguistics and Heaps' law in information retrieval are different formulations of the same phenomenon. Stated briefly and in linguistic terms they state that vocabularies' sizes are concave increasing power laws of texts' sizes. This study investigates these laws from a purely mathematical and informetric point of view. A general informetric argument shows that the problem of proving these laws is, in fact, ill-posed. Using the more general terminology of sources and items, the author shows by presenting exact formulas from Lotkaian informetrics that the total number T of sources is not only a function of the total number A of items, but is also a function of several parameters (e.g., the parameters occurring in Lotka's law). Consequently, it is shown that a fixed T(or A) value can lead to different possible A (respectively, T) values. Limiting the T(A)-variability to increasing samples (e.g., in a text as done in linguistics) the author then shows, in a purely mathematical way, that for large sample sizes T~ A**phi, where phi is a constant, phi < 1 but close to 1, hence roughly, Heaps' or Herdan's law can be proved without using any linguistic or informetric argument. The author also shows that for smaller samples, a is not a constant but essentially decreases as confirmed by practical examples. Finally, an exact informetric argument on random sampling in the items shows that, in most cases, T= T(A) is a concavely increasing function, in accordance with practical examples.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 1738) [ClassicSimilarity], result of:
          0.025790809 = score(doc=1738,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 1738, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1738)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Purpose - The present study aims to investigate how textual features, depth of citation treatment, reasons for citation, and relationships between citers and citees predict author-rated citation importance. Design/methodology/approach - A total of 49 biology and 50 psychology authors assessed the importance, reason for citation, and relationship to the cited author for each cited reference in his or her own recently published empirical article. Participants performed their evaluations on individualized web-based surveys. Findings - The paper finds that certain textual features, such as citation frequency, citation length, and citation location, as well as author-stated reasons for citation predicted ratings of importance, but the strength of the relationship often depended on citation features in the article as a whole. The relationship between objective citation features and author-rated importance also tended to be weaker for self-citations. Research limitations/implications - The study sample included authors of relatively long empirical articles with a minimum of 35 cited references. There were relatively few disciplinary differences, which suggests that citation behavior in psychology may be similar to that in natural science disciplines. Future studies should involve authors from other disciplines employing diverse referencing patterns in articles of varying lengths and types. Originality/value - Findings of the study have enabled a comprehensive, profound level of understanding of citation behaviors of biology and psychology authors. It uncovered a number of unique characteristics in authors' citation evaluations, such as article-level context effects and rule- versus affective-based judgments. The paper suggests possible implications for developing retrieval algorithms based on automatically predicted importance of cited references.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 4143) [ClassicSimilarity], result of:
          0.025790809 = score(doc=4143,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 4143, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4143)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.

0.0028656456 = product of:
  0.008596936 = sum of:
    0.008596936 = product of:
      0.025790809 = sum of:
        0.025790809 = weight(_text_:retrieval in 389) [ClassicSimilarity], result of:
          0.025790809 = score(doc=389,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.16710453 = fieldWeight in 389, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=389)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

In this article, we explore how strongly author name disambiguation (AND) affects the results of an author-based citation analysis study, and identify conditions under which the traditional simplified approach of using surnames and first initials may suffice in practice. We compare author citation ranking and cocitation mapping results in the stem cell research field from 2004 to 2009 using two AND approaches: the traditional simplified approach of using author surname and first initial and a sophisticated algorithmic approach. We find that the traditional approach leads to extremely distorted rankings and substantially distorted mappings of authors in this field when based on first- or all-author citation counting, whereas last-author-based citation ranking and cocitation mapping both appear relatively immune to the author name ambiguity problem. This is largely because Romanized names of Chinese and Korean authors, who are very active in this field, are extremely ambiguous, but few of these researchers consistently publish as last authors in bylines. We conclude that a more earnest effort is required to deal with the author name ambiguity problem in both citation analysis and information retrieval, especially given the current trend toward globalization. In the stem cell research field, in which laboratory heads are traditionally listed as last authors in bylines, last-author-based citation ranking and cocitation mapping using the traditional approach to author name disambiguation may serve as a simple workaround, but likely at the price of largely filtering out Chinese and Korean contributions to the field as well as important contributions by young researchers.