Search (6 results, page 1 of 1)

0.021194674 = product of:
  0.04238935 = sum of:
    0.04238935 = product of:
      0.0847787 = sum of:
        0.0847787 = weight(_text_:22 in 234) [ClassicSimilarity], result of:
          0.0847787 = score(doc=234,freq=2.0), product of:
            0.18260197 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052144732 = queryNorm
            0.46428138 = fieldWeight in 234, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=234)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Pages

22 S

0.012960912 = product of:
  0.025921823 = sum of:
    0.025921823 = product of:
      0.051843647 = sum of:
        0.051843647 = weight(_text_:data in 2417) [ClassicSimilarity], result of:
          0.051843647 = score(doc=2417,freq=18.0), product of:
            0.16488427 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.052144732 = queryNorm
            0.31442446 = fieldWeight in 2417, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0234375 = fieldNorm(doc=2417)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This is a report about the use and misuse of citation data in the assessment of scientific research. The idea that research assessment must be done using "simple and objective" methods is increasingly prevalent today. The "simple and objective" methods are broadly interpreted as bibliometrics, that is, citation data and the statistics derived from them. There is a belief that citation statistics are inherently more accurate because they substitute simple numbers for complex judgments, and hence overcome the possible subjectivity of peer review. But this belief is unfounded. - Relying on statistics is not more accurate when the statistics are improperly used. Indeed, statistics can mislead when they are misapplied or misunderstood. Much of modern bibliometrics seems to rely on experience and intuition about the interpretation and validity of citation statistics. - While numbers appear to be "objective", their objectivity can be illusory. The meaning of a citation can be even more subjective than peer review. Because this subjectivity is less obvious for citations, those who use citation data are less likely to understand their limitations. - The sole reliance on citation data provides at best an incomplete and often shallow understanding of research - an understanding that is valid only when reinforced by other judgments. Numbers are not inherently superior to sound judgments.
Using citation data to assess research ultimately means using citation-based statistics to rank things.journals, papers, people, programs, and disciplines. The statistical tools used to rank these things are often misunderstood and misused. - For journals, the impact factor is most often used for ranking. This is a simple average derived from the distribution of citations for a collection of articles in the journal. The average captures only a small amount of information about that distribution, and it is a rather crude statistic. In addition, there are many confounding factors when judging journals by citations, and any comparison of journals requires caution when using impact factors. Using the impact factor alone to judge a journal is like using weight alone to judge a person's health. - For papers, instead of relying on the actual count of citations to compare individual papers, people frequently substitute the impact factor of the journals in which the papers appear. They believe that higher impact factors must mean higher citation counts. But this is often not the case! This is a pervasive misuse of statistics that needs to be challenged whenever and wherever it occurs. -For individual scientists, complete citation records can be difficult to compare. As a consequence, there have been attempts to find simple statistics that capture the full complexity of a scientist's citation record with a single number. The most notable of these is the h-index, which seems to be gaining in popularity. But even a casual inspection of the h-index and its variants shows that these are naive attempts to understand complicated citation records. While they capture a small amount of information about the distribution of a scientist's citations, they lose crucial information that is essential for the assessment of research.
The validity of statistics such as the impact factor and h-index is neither well understood nor well studied. The connection of these statistics with research quality is sometimes established on the basis of "experience." The justification for relying on them is that they are "readily available." The few studies of these statistics that were done focused narrowly on showing a correlation with some other measure of quality rather than on determining how one can best derive useful information from citation data. We do not dismiss citation statistics as a tool for assessing the quality of research.citation data and statistics can provide some valuable information. We recognize that assessment must be practical, and for this reason easily-derived citation statistics almost surely will be part of the process. But citation data provide only a limited and incomplete view of research quality, and the statistics derived from citation data are sometimes poorly understood and misused. Research is too important to measure its value with only a single coarse tool. We hope those involved in assessment will read both the commentary and the details of this report in order to understand not only the limitations of citation statistics but also how better to use them. If we set high standards for the conduct of science, surely we should set equally high standards for assessing its quality.

0.012471643 = product of:
  0.024943287 = sum of:
    0.024943287 = product of:
      0.049886573 = sum of:
        0.049886573 = weight(_text_:data in 2436) [ClassicSimilarity], result of:
          0.049886573 = score(doc=2436,freq=6.0), product of:
            0.16488427 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.052144732 = queryNorm
            0.30255508 = fieldWeight in 2436, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2436)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

RDA provides a set of guidelines and instructions on formulating data to support resource discovery. The data created using RDA to describe a resource are designed to assist users performing the following tasks: find-i.e., to find resources that correspond to the user's stated search criteria: identify-i.e., to confirm that the resource described corresponds to the resource sought, or to distinguish between two or more resources with similar characteristics select-i.e., to select a resource that is appropriate to the user's needs obtain-i.e., to acquire or access the resource described. The data created using RDA to describe an entity associated with a resource (a person, family, corporate body, concept, etc.) are designed to assist users performing the following tasks: find-i.e., to find information on that entity and on resources associated with the entity identify-i.e., to confirm that the entity described corresponds to the entity sought, or to distinguish between two or more entities with similar names, etc. clarify-i.e., to clarify the relationship between two or more such entities, or to clarify the relationship between the entity described and a name by which that entity is known understand-i.e., to understand why a particular name or title, or form of name or title, has been chosen as the preferred name or title for the entity.

0.008640608 = product of:
  0.017281216 = sum of:
    0.017281216 = product of:
      0.03456243 = sum of:
        0.03456243 = weight(_text_:data in 1271) [ClassicSimilarity], result of:
          0.03456243 = score(doc=1271,freq=8.0), product of:
            0.16488427 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.052144732 = queryNorm
            0.2096163 = fieldWeight in 1271, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0234375 = fieldNorm(doc=1271)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The future of bibliographic control will be collaborative, decentralized, international in scope, and Web-based. Its realization will occur in cooperation with the private sector, and with the active collaboration of library users. Data will be gathered from multiple sources; change will happen quickly; and bibliographic control will be dynamic, not static. The underlying technology that makes this future possible and necessary-the World Wide Web-is now almost two decades old. Libraries must continue the transition to this future without delay in order to retain their relevance as information providers. The Working Group on the Future of Bibliographic Control encourages the library community to take a thoughtful and coordinated approach to effecting significant changes in bibliographic control. Such an approach will call for leadership that is neither unitary nor centralized. Nor will the responsibility to provide such leadership fall solely to the Library of Congress (LC). That said, the Working Group recognizes that LC plays a unique role in the library community of the United States, and the directions that LC takes have great impact on all libraries. We also recognize that there are many other institutions and organizations that have the expertise and the capacity to play significant roles in the bibliographic future. Wherever possible, those institutions must step forward and take responsibility for assisting with navigating the transition and for playing appropriate ongoing roles after that transition is complete. To achieve the goals set out in this document, we must look beyond individual libraries to a system wide deployment of resources. We must realize efficiencies in order to be able to reallocate resources from certain lower-value components of the bibliographic control ecosystem into other higher-value components of that same ecosystem. The recommendations in this report are directed at a number of parties, indicated either by their common initialism (e.g., "LC" for Library of Congress, "PCC" for Program for Cooperative Cataloging) or by their general category (e.g., "Publishers," "National Libraries"). When the recommendation is addressed to "All," it is intended for the library community as a whole and its close collaborators.
The Library of Congress must begin by prioritizing the recommendations that are directed in whole or in part at LC. Some define tasks that can be achieved immediately and with moderate effort; others will require analysis and planning that will have to be coordinated broadly and carefully. The Working Group has consciously not associated time frames with any of its recommendations. The recommendations fall into five general areas: 1. Increase the efficiency of bibliographic production for all libraries through increased cooperation and increased sharing of bibliographic records, and by maximizing the use of data produced throughout the entire "supply chain" for information resources. 2. Transfer effort into higher-value activity. In particular, expand the possibilities for knowledge creation by "exposing" rare and unique materials held by libraries that are currently hidden from view and, thus, underused. 3. Position our technology for the future by recognizing that the World Wide Web is both our technology platform and the appropriate platform for the delivery of our standards. Recognize that people are not the only users of the data we produce in the name of bibliographic control, but so too are machine applications that interact with those data in a variety of ways. 4. Position our community for the future by facilitating the incorporation of evaluative and other user-supplied information into our resource descriptions. Work to realize the potential of the FRBR framework for revealing and capitalizing on the various relationships that exist among information resources. 5. Strengthen the library profession through education and the development of metrics that will inform decision-making now and in the future. The Working Group intends what follows to serve as a broad blueprint for the Library of Congress and its colleagues in the library and information technology communities for extending and promoting access to information resources.

0.007200507 = product of:
  0.014401014 = sum of:
    0.014401014 = product of:
      0.028802028 = sum of:
        0.028802028 = weight(_text_:data in 2398) [ClassicSimilarity], result of:
          0.028802028 = score(doc=2398,freq=2.0), product of:
            0.16488427 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.052144732 = queryNorm
            0.17468026 = fieldWeight in 2398, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2398)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

It may be jumping the gun a bit to review this publication before it is actually published, but we are nothing if not current here at Current Cites, so we will do it anyway (so sue us!). This publication-in-process is a joint effort of the Visual Resources Association and the Digital Library Federation. It aims to "provide guidelines for selecting, ordering, and formatting data used to populate catalog records" relating to cultural works. Although this work is far from finished (Chapters 1, 2, 7, and 9 are available, as well as front and back matter), the authors are making it available so pratictioners can use it and respond with information about how it can be improved to better aid their work. A stated goal is to publish it in print at some point in the future. Besides garnering support from the organizations named above as well as the Getty, the Mellon Foundation and others, the effort is being guided by experienced professionals at the top of their field. Get the point? If you're involved with creating metadata relating to any type of cultural object and/or images of such, this will need to be either on your bookshelf, or bookmarked in your browser, or both

0.005760405 = product of:
  0.01152081 = sum of:
    0.01152081 = product of:
      0.02304162 = sum of:
        0.02304162 = weight(_text_:data in 172) [ClassicSimilarity], result of:
          0.02304162 = score(doc=172,freq=2.0), product of:
            0.16488427 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.052144732 = queryNorm
            0.1397442 = fieldWeight in 172, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.03125 = fieldNorm(doc=172)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.