Search (11 results, page 1 of 1)

0.045676924 = product of:
  0.09135385 = sum of:
    0.09135385 = sum of:
      0.04885695 = weight(_text_:research in 1521) [ClassicSimilarity], result of:
        0.04885695 = score(doc=1521,freq=6.0), product of:
          0.1491455 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.05227703 = queryNorm
          0.3275791 = fieldWeight in 1521, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.046875 = fieldNorm(doc=1521)
      0.042496894 = weight(_text_:22 in 1521) [ClassicSimilarity], result of:
        0.042496894 = score(doc=1521,freq=2.0), product of:
          0.18306525 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.05227703 = queryNorm
          0.23214069 = fieldWeight in 1521, 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=1521)
  0.5 = coord(1/2)

Abstract

Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation's value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.

Date

22. 8.2014 16:52:04

0.013140426 = product of:
  0.026280852 = sum of:
    0.026280852 = product of:
      0.052561704 = sum of:
        0.052561704 = weight(_text_:research in 4461) [ClassicSimilarity], result of:
          0.052561704 = score(doc=4461,freq=10.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.352419 = fieldWeight in 4461, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4461)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Software programs are among the most important tools in data-driven research. The popularity of well-known packages and corresponding large numbers of citations received bear testimony of the contribution of scientific software to academic research. Yet software is not generally recognized as an academic outcome. In this study, a usage-based model is proposed with varied indicators including citations, mentions, and downloads to measure the importance of scientific software. We performed an investigation on a sample of international bioinformatics research articles, and on a sample from the Chinese community. Our analysis shows that scientists in the field of bioinformatics rely heavily on scientific software: the major differences between the international community and the Chinese example being how scientific packages are mentioned in publications and the time gap between the introduction of a package and its use. Biologists publishing in international journals tend to apply the latest tools earlier; Chinese scientists publishing in Chinese tend to follow later. Further, journals with higher impact factors tend to publish articles applying the latest tools earlier.

0.012214238 = product of:
  0.024428476 = sum of:
    0.024428476 = product of:
      0.04885695 = sum of:
        0.04885695 = weight(_text_:research in 565) [ClassicSimilarity], result of:
          0.04885695 = score(doc=565,freq=6.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.3275791 = fieldWeight in 565, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046875 = fieldNorm(doc=565)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The research provides (1) an account of the construction of a new Scientific Argument Ontology (SAO), (2) a statistical analysis of 40 articles from both fields of Library and Information Science and Biomedical Research, and (3) a summary of important differences between the article structures common to each respective field of study and characteristics of their contents as revealed by applying SAO to conduct qualitative analysis. Ontology coverage ratios as well as the ratios of different classes and evidence types were calculated in the analysis. The results show a comprehensive coverage of SAO, while also indicate that the ontological construction of scientific arguments should fully consider the characteristics of their disciplines and fields in order to better facilitate extraction, discovery and retrieval.

0.0101785315 = product of:
  0.020357063 = sum of:
    0.020357063 = product of:
      0.040714126 = sum of:
        0.040714126 = weight(_text_:research in 463) [ClassicSimilarity], result of:
          0.040714126 = score(doc=463,freq=6.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.2729826 = fieldWeight in 463, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=463)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The interdisciplinary field known as digital humanities (DH) is represented in various forms in the teaching and research practiced in iSchools. Building on the work of an iSchools organization committee charged with exploring digital humanities curricula, we present findings from a series of related studies exploring aspects of DH teaching, education, and research in iSchools, often in collaboration with other units and disciplines. Through a survey of iSchool programs and an online DH course registry, we investigate the various education models for DH training found in iSchools, followed by a detailed look at DH courses and curricula, explored through analysis of course syllabi and course descriptions. We take a brief look at collaborative disciplines with which iSchools cooperate on DH research projects or in offering DH education. Next, we explore DH careers through an analysis of relevant job advertisements. Finally, we offer some observations about the management and administrative challenges and opportunities related to offering a new iSchool DH program. Our results provide a snapshot of the current state of digital humanities in iSchools which may usefully inform the design and evolution of new DH programs, degrees, and related initiatives.

0.0101785315 = product of:
  0.020357063 = sum of:
    0.020357063 = product of:
      0.040714126 = sum of:
        0.040714126 = weight(_text_:research in 567) [ClassicSimilarity], result of:
          0.040714126 = score(doc=567,freq=6.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.2729826 = fieldWeight in 567, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=567)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure.

0.009972882 = product of:
  0.019945765 = sum of:
    0.019945765 = product of:
      0.03989153 = sum of:
        0.03989153 = weight(_text_:research in 1301) [ClassicSimilarity], result of:
          0.03989153 = score(doc=1301,freq=4.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.2674672 = fieldWeight in 1301, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046875 = fieldNorm(doc=1301)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information.

0.008310735 = product of:
  0.01662147 = sum of:
    0.01662147 = product of:
      0.03324294 = sum of:
        0.03324294 = weight(_text_:research in 5980) [ClassicSimilarity], result of:
          0.03324294 = score(doc=5980,freq=4.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.22288933 = fieldWeight in 5980, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5980)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

0.008310735 = product of:
  0.01662147 = sum of:
    0.01662147 = product of:
      0.03324294 = sum of:
        0.03324294 = weight(_text_:research in 336) [ClassicSimilarity], result of:
          0.03324294 = score(doc=336,freq=4.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.22288933 = fieldWeight in 336, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=336)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta-synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse-initiation, exploration and collection, and repurposing-and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements.

0.007051893 = product of:
  0.014103786 = sum of:
    0.014103786 = product of:
      0.028207572 = sum of:
        0.028207572 = weight(_text_:research in 4927) [ClassicSimilarity], result of:
          0.028207572 = score(doc=4927,freq=2.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.18912788 = fieldWeight in 4927, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046875 = fieldNorm(doc=4927)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Recent studies have found organizational learning capacity to be a key factor in influencing organizational assimilation and exploitation of knowledge-intensive innovations. Despite its increasing importance, the impact of organizational learning capacity an technology assimilation is not well understood. Distilling from extant works an organizational learning and technology assimilation, this study identifies four components of organizational learning capacity, namely, systems orientation, organizational climate for learning orientation, knowledge acquisition and utilization orientation, and information sharing and dissemination orientation. The authors subject these components to structural equation modeling analyses to better understand their structure and dimensionality. The analyses strongly support the proposed four major dimensions underlying organizational learning capacity. Organizational learning capacity, as a higher-order factor, has a significant impact an attitude towards organizational adoption of knowledge-intensive innovations. Implications for practice and research are discussed.

0.007051893 = product of:
  0.014103786 = sum of:
    0.014103786 = product of:
      0.028207572 = sum of:
        0.028207572 = weight(_text_:research in 455) [ClassicSimilarity], result of:
          0.028207572 = score(doc=455,freq=2.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.18912788 = fieldWeight in 455, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046875 = fieldNorm(doc=455)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Digital images of cultural heritage (CH) contain rich semantic information. However, today's semantic representations of CH images fail to fully reveal the content entities and context within these vital surrogates. This paper draws on the fields of image research and digital humanities to propose a systematic methodology and a technical route for semantic enrichment of CH digital images. This new methodology systematically applies a series of procedures including: semantic annotation, entity-based enrichment, establishing internal relations, event-centric enrichment, defining hierarchy relations between properties text annotation, and finally, named entity recognition in order to ultimately provide fine-grained contextual semantic content disclosure. The feasibility and advantages of the proposed semantic enrichment methods for semantic representation are demonstrated via a visual display platform for digital images of CH built to represent the Wutai Mountain Map, a typical Dunhuang mural. This study proves that semantic enrichment offers a promising new model for exposing content at a fine-grained level, and establishing a rich semantic network centered on the content of digital images of CH.

0.005876578 = product of:
  0.011753156 = sum of:
    0.011753156 = product of:
      0.023506312 = sum of:
        0.023506312 = weight(_text_:research in 847) [ClassicSimilarity], result of:
          0.023506312 = score(doc=847,freq=2.0), product of:
            0.1491455 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.05227703 = queryNorm
            0.15760657 = fieldWeight in 847, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=847)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.