CO-CITATION BIBLIOMETRIC MODELING AS A TOOL FOR S&amp;T POLICY AND R&amp;D MANAGEMENT: ISSUES, APPLICATIONS, AND DEVELOPMENTS

Franklin, Jane; Johnston, Ron

doi:10.1016/b978-0-444-70537-2.50017-9

Cited by 39 publications

(26 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods are variously known as co-word analysis (Callon, Law, & Rip, 1986;Whittaker, 1989), citation mapping (Healey, Rothman, & Hoch, 1986;Small, 1999), and bibliometric analysis, bibliometric modeling, or bibliometric mapping (Crouch, Irvine, & Martin, 1986;Franklin & Johnston, 1988;King, 1987;Noyons, 2001Noyons, , 2005. In addition to the usefulness of these methods for evaluation and measurement of science, they allow for the creation of visual maps that represent the scholarly activities and the relationships that exist in a given domain.…”

Section: Introductionmentioning

confidence: 99%

A bibliometric mapping of the structure of STEM education using co‐word analysis

Assefa

Rorissa

2013

J Am Soc Inf Sci Tec

View full text Add to dashboard Cite

STEM, a set of fields that includes science, technology, engineering, and mathematics; allied disciplines ranging from environmental, agricultural, and earth sciences to life science and computer science; and education and training in these fields, is clearly at the top of the list of priority funding areas for governments, including the United States government. The U.S. has 11 federal agencies dedicated to supporting programs and providing funding for research and curriculum development. The domain of STEM education has significant implications in preparing the desired workforce with the requisite knowledge, developing appropriate curricula, providing teachers the necessary professional development, focusing research dollars on areas that have maximum impact, and developing national educational policy and standards. A complex undertaking such as STEM education, which attracts interest and valuable resources from a number of stakeholders needs to be well understood. In light of this, we attempt to describe the underlying structure of STEM education, its core areas, and their relationships through co-word analyses of the titles, keywords, and abstracts of the relevant literature using visualization and bibliometric mapping tools. Implications are drawn with respect to the nature of STEM education as well as curriculum and policy development.

show abstract

Section: Introductionmentioning

confidence: 99%

A bibliometric mapping of the structure of STEM education using co‐word analysis

Assefa

Rorissa

2013

J Am Soc Inf Sci Tec

View full text Add to dashboard Cite

show abstract

“…Obtaining this information by examining citations and co-citations restricts the types of documents that can be analyzed (essentially published papers) and requires the additional assumption that the themes of two articles cocited many times by authors must be strongly related. While the co-citation proponents claim that "many potentially useful applications have been demonstrated" [Franklin, 1988], others conclude that "results of co-citation cluster analyses cannot be taken seriously as evidence relevant to the formulation of research policy" [Oberski, 1988].…”

Section: Co-citation and Co-nomination Analysis C-1 Co-citation Amentioning

confidence: 99%

“…In co-citation analysis, the frequencies with which references in published documents are cited together are obtained, and are eventually used to generate maps of clusters of cohesive research themes. Co-citation analysis was developed about three decades ago, when the Science Citation Index became more readily available for computer analysis, and it has spawned a number of studies and reviews, a few of which are listed here [Small, 1973;Small, 1977;Small, 1978;Garfield, 1978;Small, 1980;Small, 1985a;Small, 1985b;Small, 1986;Franklin, 1988;Oberski, 1988].…”

Section: Co-citation and Co-nomination Analysis C-1 Co-citation Amentioning

confidence: 99%

Science and Technology Text Mining: Origins of Database Tomography and Multi-Word Phrase Clustering

Kostoff¹

2003

View full text Add to dashboard Cite

A. ABSTRACTThis report initially describes the motivations for co-word analysis in support of research policy formulation and research implementation evaluation. It places coword analysis in perspective to other co-occurrence techniques such as cocitation and co-nomination analyses. It then traces the origins of co-word analysis in computational linguistics, describes in detail the development of co-word analysis for research evaluation, and concludes by presenting a new approach to co-word analysis for research evaluation (Database Tomography). The report shows that this new approach to co-word analysis, which requires no index or key words but deals with text directly, is a useful tool for scanning large bodies of text. It can identify pervasive thrust areas and their interrelationships, and serve as a starting point for further in-depth analysis of the text. Its value increases as: 1) the size of text increases and 2) the breadth of topical areas covered by the text increases beyond the expertise of a moderate number of panels of experts. A single link clustering example is shown that represents the first use of multi-word technical phrases in modern clustering.(The views in this report are solely those of the author and do not represent the views of the Department of the Navy or any of its components)

show abstract

“…The Tech OASIS development effort represents a collaborative partnership itself, which includes Search Technology, Inc. as the prime contractor and sub-contractors, the Georgia Tech Technology Policy and Assessment Center (TPAC) and Intelligent Information Services Corporation (IISC). [3]; TOTAL GROUP RECORDS [10] Automotive engineering [3]; Metallographic microstructure [3]; TOTAL GROUP RECORDS [5] Magnesium castings [5]; Industrial applications [4]; Machining [3]; TOTAL GROUP RECORDS [9] Tensile strength [5]; Corrosion Resisting [4]; TOTAL GROUP RECORDS [7] Yield stress [4]; Composition effects [3]; TOTAL GROUP RECORDS [5] Plastics applications [3]; Powder metals [3]; TOTAL GROUP RECORDS [4] Sheet metal [4]; Age hardening [3]; TOTAL GROUP RECORDS [6] *OTHER* TOTAL GROUP RECORDS [21] …”

Section: Methodsmentioning

confidence: 99%

“…• "KDD" -Knowledge Discovery in Databases [21] [22] • "scientometrics" [3][11] [16] [17] • "co-citation analysis" [5] • "text data mining" [9] • "technology opportunities analysis" [12] [15] These approaches can help technology managers answer varied questions about an emerging technology, for instance:…”

Section: Introductionmentioning

confidence: 99%

Requirements-based knowledge discovery for technology management

Watts¹,

Porter²

PICMET '01. Portland International Conference on Management of Engineering and Technology. Proceedings Vol.1: Book of Summaries

View full text Add to dashboard Cite

-Exploiting information resources, particularly diverse research & development databases, presents opportunities to enhance technology management. Many such databases [e.g., Science Citation Index, Business Index] offer useful index fields to help you access documents. However, these indexes sometimes fail to address your requirements effectively. We present an approach that allows you to extract and categorize desired information from particular datasets that lack effective indexing for your purposes. We illustrate for a U.S. Patents dataset on lightweight automotive materials.

show abstract

CO-CITATION BIBLIOMETRIC MODELING AS A TOOL FOR S&T POLICY AND R&D MANAGEMENT: ISSUES, APPLICATIONS, AND DEVELOPMENTS

Cited by 39 publications

References 32 publications

A bibliometric mapping of the structure of STEM education using co‐word analysis

A bibliometric mapping of the structure of STEM education using co‐word analysis

Science and Technology Text Mining: Origins of Database Tomography and Multi-Word Phrase Clustering

Requirements-based knowledge discovery for technology management

Contact Info

Product

Resources

About