A grande quantidade de artigos científicos disponíveis on-line faz com que seja mais fácil para estudantes e pesquisadores reutilizarem texto de outros autores, e torna mais difícil a verificação da originalidade de um determinado texto. Reutilizar texto sem creditar a fonte é considerado plágio. Uma série de estudos relatam a alta prevalência de plágio no meio acadêmico e científico. Como consequência, inúmeras instituições e pesquisadores têm se dedicado à elaboração de sistemas para automatizar o processo de verificação de plágio. A maioria dos trabalhos existentes baseia-se na análise da similaridade do conteúdo textual dos documentos para avaliar a existência de plágio. Mais recentemente, foram propostas métricas de similaridade que desconsideram o texto e analisam apenas as citações e/ou referências bibliográficas compartilhadas entre documentos. Entretanto, casos em que o autor não referencia a fonte original pode passar despercebido pelas métricas baseadas apenas na análise de referências/citações. Neste contexto, a solução proposta é baseada na hipótese de que a combinação de métricas de similaridade de conteúdo e de citações/referências pode melhorar a qualidade da detecção de plágio. Duas formas de combinação são propostas: (i) os escores produzidos pelas métricas de similaridade são utilizados para ranqueamento dos pares de documentos e (ii) os escores das métricas são utilizados para construir vetores de características que serão usados por algoritmos de Aprendizagem de Máquina para classificar os documentos. Os experimentos foram realizados com conjuntos de dados reais de artigos científicos. A avaliação experimental mostra que a hipótese foi confirmada quando a combinação das métricas de similaridade usando Aprendizagem de Máquina é comparada com a combinação simples. Ainda, ambas as combinações apresentaram ganhos quando comparadas com as métricas aplicadas de forma individual. / The large amount of scientific documents available online makes it easier for students and researchers reuse text from other authors, and makes it difficult to verify the originality of a given text. Reusing text without crediting the source is considered plagiarism. A number of studies have reported on the high prevalence of plagiarism in academia. As a result, many institutions and researchers have developed systems that automate the plagiarism detection process. Most of the existing work is based on the analysis of the similarity of the textual content of documents to assess the existence of plagiarism. More recently, similarity metrics that ignore the text and just analyze the citations and/or references shared between documents have been proposed. However, cases in which the author does not reference the original source may go unnoticed by metrics based only on the references/citations analysis. In this context, the proposed solution is based on the hypothesis that the combination of content similarity metrics and references/citations can improve the quality of plagiarism detection. Two forms of combination are proposed: (i) scores produced by the similarity metrics are used to ranking of pairs of documents and (ii) scores of metrics are used to construct feature vectors that are used by algorithms machine learning to classify documents. The experiments were performed with real data sets of papers. The experimental evaluation shows that the hypothesis was confirmed when the combination of the similarity metrics using machine learning is compared with the simple combining. Also, both compounds showed gains when compared with the metrics applied individually.

A grande quantidade de artigos científicos disponíveis on-line faz com que seja mais fácil para estudantes e pesquisadores reutilizarem texto de outros autores, e torna mais difícil a verificação da originalidade de um determinado texto. Reutilizar texto sem creditar a fonte é considerado plágio. Uma série de estudos relatam a alta prevalência de plágio no meio acadêmico e científico. Como consequência, inúmeras instituições e pesquisadores têm se dedicado à elaboração de sistemas para automatizar o processo de verificação de plágio. A maioria dos trabalhos existentes baseia-se na análise da similaridade do conteúdo textual dos documentos para avaliar a existência de plágio. Mais recentemente, foram propostas métricas de similaridade que desconsideram o texto e analisam apenas as citações e/ou referências bibliográficas compartilhadas entre documentos. Entretanto, casos em que o autor não referencia a fonte original pode passar despercebido pelas métricas baseadas apenas na análise de referências/citações. Neste contexto, a solução proposta é baseada na hipótese de que a combinação de métricas de similaridade de conteúdo e de citações/referências pode melhorar a qualidade da detecção de plágio. Duas formas de combinação são propostas: (i) os escores produzidos pelas métricas de similaridade são utilizados para ranqueamento dos pares de documentos e (ii) os escores das métricas são utilizados para construir vetores de características que serão usados por algoritmos de Aprendizagem de Máquina para classificar os documentos. Os experimentos foram realizados com conjuntos de dados reais de artigos científicos. A avaliação experimental mostra que a hipótese foi confirmada quando a combinação das métricas de similaridade usando Aprendizagem de Máquina é comparada com a combinação simples. Ainda, ambas as combinações apresentaram ganhos quando comparadas com as métricas aplicadas de forma individual. / The large amount of scientific documents available online makes it easier for students and researchers reuse text from other authors, and makes it difficult to verify the originality of a given text. Reusing text without crediting the source is considered plagiarism. A number of studies have reported on the high prevalence of plagiarism in academia. As a result, many institutions and researchers have developed systems that automate the plagiarism detection process. Most of the existing work is based on the analysis of the similarity of the textual content of documents to assess the existence of plagiarism. More recently, similarity metrics that ignore the text and just analyze the citations and/or references shared between documents have been proposed. However, cases in which the author does not reference the original source may go unnoticed by metrics based only on the references/citations analysis. In this context, the proposed solution is based on the hypothesis that the combination of content similarity metrics and references/citations can improve the quality of plagiarism detection. Two forms of combination are proposed: (i) scores produced by the similarity metrics are used to ranking of pairs of documents and (ii) scores of metrics are used to construct feature vectors that are used by algorithms machine learning to classify documents. The experiments were performed with real data sets of papers. The experimental evaluation shows that the hypothesis was confirmed when the combination of the similarity metrics using machine learning is compared with the simple combining. Also, both compounds showed gains when compared with the metrics applied individually.

A grande quantidade de artigos científicos disponíveis on-line faz com que seja mais fácil para estudantes e pesquisadores reutilizarem texto de outros autores, e torna mais difícil a verificação da originalidade de um determinado texto. Reutilizar texto sem creditar a fonte é considerado plágio. Uma série de estudos relatam a alta prevalência de plágio no meio acadêmico e científico. Como consequência, inúmeras instituições e pesquisadores têm se dedicado à elaboração de sistemas para automatizar o processo de verificação de plágio. A maioria dos trabalhos existentes baseia-se na análise da similaridade do conteúdo textual dos documentos para avaliar a existência de plágio. Mais recentemente, foram propostas métricas de similaridade que desconsideram o texto e analisam apenas as citações e/ou referências bibliográficas compartilhadas entre documentos. Entretanto, casos em que o autor não referencia a fonte original pode passar despercebido pelas métricas baseadas apenas na análise de referências/citações. Neste contexto, a solução proposta é baseada na hipótese de que a combinação de métricas de similaridade de conteúdo e de citações/referências pode melhorar a qualidade da detecção de plágio. Duas formas de combinação são propostas: (i) os escores produzidos pelas métricas de similaridade são utilizados para ranqueamento dos pares de documentos e (ii) os escores das métricas são utilizados para construir vetores de características que serão usados por algoritmos de Aprendizagem de Máquina para classificar os documentos. Os experimentos foram realizados com conjuntos de dados reais de artigos científicos. A avaliação experimental mostra que a hipótese foi confirmada quando a combinação das métricas de similaridade usando Aprendizagem de Máquina é comparada com a combinação simples. Ainda, ambas as combinações apresentaram ganhos quando comparadas com as métricas aplicadas de forma individual. / The large amount of scientific documents available online makes it easier for students and researchers reuse text from other authors, and makes it difficult to verify the originality of a given text. Reusing text without crediting the source is considered plagiarism. A number of studies have reported on the high prevalence of plagiarism in academia. As a result, many institutions and researchers have developed systems that automate the plagiarism detection process. Most of the existing work is based on the analysis of the similarity of the textual content of documents to assess the existence of plagiarism. More recently, similarity metrics that ignore the text and just analyze the citations and/or references shared between documents have been proposed. However, cases in which the author does not reference the original source may go unnoticed by metrics based only on the references/citations analysis. In this context, the proposed solution is based on the hypothesis that the combination of content similarity metrics and references/citations can improve the quality of plagiarism detection. Two forms of combination are proposed: (i) scores produced by the similarity metrics are used to ranking of pairs of documents and (ii) scores of metrics are used to construct feature vectors that are used by algorithms machine learning to classify documents. The experiments were performed with real data sets of papers. The experimental evaluation shows that the hypothesis was confirmed when the combination of the similarity metrics using machine learning is compared with the simple combining. Also, both compounds showed gains when compared with the metrics applied individually.

Abstract The research was focused on the international diffusion of a national zoological journal, Annales Zoologici Fennici. The purpose of the study was to apply diffusion of innovations research as a theoretical framework of a diffusion study conducted through the bibliometric methodology. The following research questions were studied: to which countries has Annales Zoologici Fennici diffused, what are the attributes of Annales Zoologici Fennici as an innovation, and what theoretical concepts adopted from diffusion research can be considered as useful in a bibliometric study of the diffusion of a scientific journal. The following concepts were examined: innovation, diffusion, and the relative advantage, compatibility, complexity, and trialability of an innovation. The international diffusion of Annales Zoologici Fennici was studied through citation analysis and an analysis of the national distribution of authors whose articles had been published in the journal, and as the attributes of innovation, the compatibility, complexity and observability of Annales Zoologici Fennici were examined. Both the study of citations and authors showed a growth of international awareness of Annales Zoologici Fennici during the study period, 1974–1996. The most important directions of diffusion were to the boreal areas. The diffusion was most marked in the horizontal direction, but an element of hierarchical diffusion was also noted. The compatibility of the journal as well as a decrease of complexity were found to have an influence on diffusion. Bibliometric methods were able to represent the international diffusion of a scientific journal. / Tiivistelmä Tutkimuksen aihe oli kansallisen tieteellisen lehden kansainvälinen diffuusio. Tutkimuksessa tarkasteltiin suomalaisen, eläintieteellisen Annales Zoologici Fennici -lehden kansainvälisen aseman kehitystä innovaatioiden diffuusio-näkökulmasta bibliometrisin tutkimusmenetelmin. Tutkimuksen tarkoituksena oli lisätä ymmärrystä tieteellisen lehden kansainvälisestä leviämisestä. Lisäksi tutkimuksen tarkoituksena oli tarkastella innovaatioiden diffuusiotutkimuksesta omaksuttujen käsitteiden soveltuvuutta tieteellisen lehden diffuusiota koskevaan tutkimukseen, jossa käytetään bibliometrisiä tutkimusmenetelmiä. Vastausta haettiin seuraaviin tutkimuskysymyksiin: mihin maihin Annales Zoologici Fennicin diffuusio on suuntautunut, millaiset ovat Annales Zoologici Fennicin ominaisuudet innovaationa ja miten hyvin innovaatioiden diffuusiotutkimuksen piirissä kehitetyt käsitteet soveltuvat tieteellisen lehden diffuusion tarkasteluun bibliometrisin menetelmin. Tutkimuksessa tarkasteltiin seuraavia käsitteitä: innovaatio, diffuusio sekä innovaation suhteellinen etu, yhteensopivuus, monimutkaisuus ja näkyvyys. Kansainvälisen diffuusion tarkastelu perustui viittausanalyysiin ja Annales Zoologici Fennicissä artikkeleita julkaisseiden kirjoittajien kansallisen jakauman tarkasteluun. Annales Zoologici Fennicin saamista viittauksista tarkasteltiin kokonaismäärää ja viittausten alkuperää. Lisäksi tarkasteltiin Annales Zoologici Fennicin diffuusion hierarkkisuutta vertaamalla eri maista tulleiden viittausten ja artikkeleiden määrää näiden maiden panokseen eläintieteellisinä julkaisijoina. Annales Zoologici Fennici muuttui tarkastellun ajanjakson, vuosien 1974–1996 kuluessa, selvästi kotimaisesta tieteellisestä lehdestä aiempaa kansainvälisemmäksi. Lehden kansainvälinen diffuusio ilmeni sekä sen saamien viittausten määrän kasvuna että sen julkaisemien ulkomaisten artikkeleiden osuuden suhteellisena lisääntymisenä. Annales Zoologici Fennicin diffuusio on voimakkaimmin suuntautunut boreaalisille alueille. Muutoksiin liittyy Annales Zoologici Fennicin yhteensopivuuden laajeneminen ja monimutkaisuuden väheneminen. Diffuusiotutkimuksesta omaksutut käsitteet ja bibliometriset menetelmät soveltuivat tieteellisen lehden kansainvälistä diffuusiota koskevaan tutkimukseen.

In vergangenen Publikationen wurden bereits verschiedene Zitierungsanalysen durchgeführt. Jedoch stets auf unterschiedlichen Datenquellen, was einen direkten Vergleich der jeweiligen Ergebnisse verhindert. Ziel dieser Arbeit soll es sein, eine Grundlage zur flexiblen Durchführung von Zitierungsanalysen zu schaffen. Durch die Entwicklung eines Data Warehouses sollen verschiedene Datenquellen integriert und konsolidiert werden, um eine Vielfalt von Analyseperspektiven und Berechnungsverfahren auf einem einheitlichen Datenbestand zu ermöglichen. Dabei wird insbesondere auf die Besonderheiten bei der Nutzung von Webdatenquellen, als wie verschiedene Methoden zur Datenbereinigung eingegangen.

This study conducted a bibliometric analysis for the purpose of identifying the crucial leadership constructs that best explain and/or define effective educational leadership in two decades (1990-2000 and 2001-2010). The study reviewed top authors in educational leadership and analyzed their top cited works to identify leadership constructs which were (a) unique to leadership works within the 1990-2000 decade, (b) unique to the 2001-2010 decade, and c) similar or different between the two decades. The study found that the leadership constructs did evolve and shift during the study period and addressed the changing demands of individuals, educational organizations, and the external environment. Crucial educational leadership constructs were the product of the efforts of researchers in educational leadership to promote effective school leadership, improve learning outcomes and student performance, and create beneficial organizational results. The findings of the study highlight the potential impact and benefit of the continually upgrading and refreshing the understanding, training, and preparation of current and future school leaders.

While many historical articles and chapters on the foundations of Instructional Design and Technology (IDT) have painted an accurate picture of the field, it has been 21 years since anyone has given emphasis to the relationships of influence among IDT scholars. Many have written on various elements of the field, emphasizing events according to their own experience, which have increased our overall understanding of IDT. However, without insight on the connections between these pieces, the field appears to be only a broad array of isolated silos, each filled with its own research interest. This research sought to discover IDT's genealogy of influence. Three main research questions were asked: "Currently, who are the most influential scholars in IDT?" "Who influenced today's most influential scholars?" and "What ideas were most influential in the scholars' relationships?" The ten most influential names in IDT were discovered, and their genealogies of influence were traced. The ideas that were most influential in the relationships between theorists were summarized into the following groups of fields: General Education, IDT (and its contributors), Psychology, Sociology and Anthropology, and Adult and Higher Education. This research found an IDT field that was very diverse but very connected. Another important result was much less expected: the prevalence of psychology as a significant influence on both past work and current big ideas. Implications are discussed, such as revising definitions of the field.

The purpose of this dissertation was to analyze the content of textbooks used in undergraduate survey courses in interdisciplinary humanities to understand the content of the curriculum and how an author's viewpoint shapes the product. By enumerating the texts and images authors and their publishers used to illustrate 20th century culture and the transition into the 21st century, the analysis generated a description of the range of perspectives from traditional to postmodern found in six sampled textbooks. Textbook content provided chronological data, while authors' source citations established identity properties of the works' contributors. Through a ranking system of authors' treatment of content and citations, the most traditional perspectives were compared to the most postmodern. Classifying cultural contributors by identity properties gave a quantitative rate of inclusion of traditionally excluded groups. A trend of increase in "diversity-infusion" was observed among all authors when the content of the textbooks was compared in chronological sequence. The qualitative differences, as constructed for this dissertation, indicate that each textbook constitutes a varied and unique representation of author perspective. The project's contribution to future research is the development of a database of art works and literary sources from the years 1900-2006 that can be used for quantification and for further study.

Yes / The extended unified theory of acceptance and use of technology (UTAUT2) is less than ten years old and has already garnered more than 6,000 citations with extensive usage in information systems and beyond. This research employed cited reference search to systematically review studies that cited UTAUT2 originating article. Based on UTAUT2 usage, the downloaded articles were classified into four categories such as: 1) General citation, 2) UTAUT2 application, 3) UTAUT2 integration, and 4) UTAUT2 extensions. Weber's (2012) theory evaluation framework revealed UTAUT2 as a robust theory on most dimensions except for parsimony arising from the complex model. UTAUT2 extensions emerged as popular UTAUT2 utilization category as researchers extended the model with context specific variables. Finally, UTAUT2 extensions were mapped to Johns' (2006) context dimensions to identify various limitations of the existing technology adoption research and to provide multi-level framework for future researchers with libraries of context dimensions.

Thesis (MSc)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Citation analysis is an important tool in the academic community. It can aid universities, funding bodies, and individual researchers to evaluate scientific work and direct resources appropriately. With the rapid growth of the scientific enterprise and the increase of online libraries that include citation analysis tools, the need for a systematic evaluation of these tools becomes more important. The research presented in this study deals with scientific research output, i.e., articles and citations, and how they can be used in bibliometrics to measure academic success. More specifically, this research analyses algorithms that rank academic entities such as articles, authors and journals to address the question of how well these algorithms can identify important and high-impact entities. A consistent mathematical formulation is developed on the basis of a categorisation of bibliometric measures such as the h-index, the Impact Factor for journals, and ranking algorithms based on Google’s PageRank. Furthermore, the theoretical properties of each algorithm are laid out. The ranking algorithms and bibliometric methods are computed on the Microsoft Academic Search citation database which contains 40 million papers and over 260 million citations that span across multiple academic disciplines. We evaluate the ranking algorithms by using a large test data set of papers and authors that won renowned prizes at numerous Computer Science conferences. The results show that using citation counts is, in general, the best ranking metric. However, for certain tasks, such as ranking important papers or identifying high-impact authors, algorithms based on PageRank perform better. As a secondary outcome of this research, publication trends across academic disciplines are analysed to show changes in publication behaviour over time and differences in publication patterns between disciplines. / AFRIKAANSE OPSOMMING: Sitasiesanalise is ’n belangrike instrument in die akademiese omgewing. Dit kan universiteite, befondsingsliggams en individuele navorsers help om wetenskaplike werk te evalueer en hulpbronne toepaslik toe te ken. Met die vinnige groei van wetenskaplike uitsette en die toename in aanlynbiblioteke wat sitasieanalise insluit, word die behoefte aan ’n sistematiese evaluering van hierdie gereedskap al hoe belangriker. Die navorsing in hierdie studie handel oor die uitsette van wetenskaplike navorsing, dit wil sê, artikels en sitasies, en hoe hulle gebruik kan word in bibliometriese studies om akademiese sukses te meet. Om meer spesifiek te wees, hierdie navorsing analiseer algoritmes wat akademiese entiteite soos artikels, outeers en journale gradeer. Dit wys hoe doeltreffend hierdie algoritmes belangrike en hoë-impak entiteite kan identifiseer. ’n Breedvoerige wiskundige formulering word ontwikkel uit ’n versameling van bibliometriese metodes soos byvoorbeeld die h-indeks, die Impak Faktor vir journaale en die rang-algoritmes gebaseer op Google se PageRank. Verder word die teoretiese eienskappe van elke algoritme uitgelê. Die rang-algoritmes en bibliometriese metodes gebruik die sitasiedatabasis van Microsoft Academic Search vir berekeninge. Dit bevat 40 miljoen artikels en meer as 260 miljoen sitasies, wat oor verskeie akademiese dissiplines strek. Ons gebruik ’n groot stel toetsdata van dokumente en outeers wat bekende pryse op talle rekenaarwetenskaplike konferensies gewen het om die rang-algoritmes te evalueer. Die resultate toon dat die gebruik van sitasietellings, in die algemeen, die beste rangmetode is. Vir sekere take, soos die gradeering van belangrike artikels, of die identifisering van hoë-impak outeers, presteer algoritmes wat op PageRank gebaseer is egter beter. ’n Sekondêre resultaat van hierdie navorsing is die ontleding van publikasie tendense in verskeie akademiese dissiplines om sodoende veranderinge in publikasie gedrag oor tyd aan te toon en ook die verskille in publikasie patrone uit verskillende dissiplines uit te wys.