Texte remanié de: Diss. Ph. D.--Yale--University graduate school, 1989. Titre de soutenance : Prophecy and quotation : a methodological study. / Bibliogr. p. [342]-377. Index.

Adult education has long been described as an emerging discipline, but there has been little empirical study of its emergence. This study examined 'emergence' by monitoring that particular knowledge base which is unique to adult education. Studies concerned with the theory and practice of adult education are a quantifiable indicator of unique knowledge about adult education. Evidence that researchers in adult education increasingly cite the work of other researchers in adult education would support the contention that the body of knowledge in adult education is growing. The articles published in Adult Education Quarterly between 1971 and 1986 were analyzed using citation analysis methodology. The frequency of citation to previous adult education studies (primary literature) as opposed to citation of studies peripheral to an adult education context (secondary literature) was determined. Distinguishing between citation categories was carried out by analyzing each title cited. The phenomenon of concern in the cited article was interpreted from the words used in the title, and coded dichotomously as 'primary literature' or 'secondary literature'. Each coded item was then recorded under named authors; thus, the cited author was credited for total frequency cited along with the coded category of writing (author of primary literature or author of secondary literature). Reliability measures performed for intra-judge consistency (recoding data), and inter-judge agreement (independent coding of data) resulted in differences in coding of less than four percent for the former, and nine percent with the latter. Validity of the procedures used in coding cited authors was tested by comparing results obtained to a 'standard'. 'Independent experts' were asked to identify from a list of the twenty most cited authors from each four volume period, those who were "primarily known for their adult education activities." The study's coding outcome of these authors compared with the expert's 'standard' resulted in greater than 75 percent agreement. With 4700 citations classified, it was found that a rising percentage of citations were to the "authors of primary literature"; from 41 percent of all citations in the first half of the study period (1971-1978), to 46 percent in the last half (1979-1986). A further breakdown showed the percentage of citations to "primary literature journals" also increasing; from 31 percent of all journals cited in '1971-1978' to 39 percent in '1979-1986'. As the scope of literature analyzed was exclusively from one North American journal, results need to be regarded with this limitation in mind. However, the empirical evidence of an increasing 'primary literature' base in adult education research suggests emergence of the field. Implications for future research are discussed in light of this and previous studies. / Education, Faculty of / Educational Studies (EDST), Department of / Graduate

Yes / The full-text of this article will be released for public view at the end of the publisher embargo, 24 months after first publication.

Although citation counts are widely used to support research evaluation, they can only reflect academic impacts, whereas research can also be useful outside academia. There is therefore a need for alternative indicators and empirical studies to evaluate them. Whilst many previous studies have investigated alternative indicators for journal articles and books, this thesis explores the importance and suitability of four web indicators for conference papers. These are readership counts from the online reference manager Mendeley and citation counts from Google Patents, Wikipedia and Google Books. To help evaluate these indicators for conference papers, correlations with Scopus citations were evaluated for each alternative indicator and compared with corresponding correlations between alternative indicators and citation counts for journal articles. Four subject areas that value conferences were chosen for the analysis: Computer Science Applications; Computer Software Engineering; Building & Construction Engineering; and Industrial & Manufacturing Engineering. There were moderate correlations between Mendeley readership counts and Scopus citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. For conference papers in Building & Construction Engineering and Industrial & Manufacturing Engineering, the correlations between Mendeley readers and citation counts are much lower than for journal articles. Thus, in fields where conferences are important, Mendeley readership counts are reasonable impact indicators for conference papers although they are better impact indicators for journal articles. Google Patent citations had low positive correlations with citation counts for both conference papers and journal articles in Software Engineering and Computer Science Applications. There were negative correlations for both conference papers and journal articles in Industrial and Manufacturing Engineering. However, conference papers in Building and Construction Engineering attracted no Google Patent citations. This suggests that there are disciplinary differences but little overall value for Google Patent citations as impact indicators in engineering fields valuing conferences. Wikipedia citations had correlations with Scopus citations that were statistically significantly positive only in Computer Science Applications, whereas the correlations were not statistically significantly different from zero in Building & Construction Engineering, Industrial & Manufacturing Engineering and Software Engineering. Conference papers were less likely to be cited in Wikipedia than journal articles were in all fields, although the difference was minor in Software Engineering. Thus, Wikipedia citations seem to have little value in engineering fields valuing conferences. Google Books citations had positive significant correlations with Scopus-indexed citations for conference papers in all fields except Building & Construction Engineering, where the correlations were not statistically significantly different from zero. Google Books citations seemed to be most valuable impact indicators in Computer Science Applications and Software Engineering, where the correlations were moderate, than in Industrial & Manufacturing Engineering, where the correlations were low. This means that Google Book citations are valuable indicators for conference papers in engineering fields valuing conferences. Although evidence from correlation tests alone is insufficient to judge the value of alternative indicators, the results suggest that Mendeley readers and Google Books citations may be useful for both journal articles and conference papers in engineering fields that value conferences, but not Wikipedia citations or Google Patent citations.

La question de la productivité et de la « qualité » de la recherche scientifique est l’une des problématiques centrales du monde économique et social du 21e siècle. Car la recherche scientifique, source d’innovations dans tous les domaines, est considérée comme la clé du développement économique et de compétitivité. La science doit également contribuer aux défis sociétaux définis dans les programmes cadre européens (H2020) par exemple, tels que la santé, la démographie et le bien-être. Afin de rationaliser les dépenses publiques de recherche et d’innovation ou encore orienter les stratégies d’investissement des bailleurs de fonds, plusieurs indicateurs ce sont développés pour mesurer les performances des entités de recherche. Désormais, nul ne peut échapper à l’évaluation, à commencer par les articles de recherche, en passant par les chercheurs, jusqu’aux institutions et aux pays (Pansu, 2013, Gingras, 2016). Faute de manque de recul méthodologique, les indicateurs quantitatifs sont parfois mal utilisés en négligeant tous les aspects liés à leur méthode de calcul/normalisation, ce qu’ils représentent ou encore les insuffisances des bases de données à partir desquelles ils sont calculés. Cette situation risque d’avoir des conséquences désastreuses sur le plan scientifique et social. Notre travail envisage d’examiner les outils de la bibliométrie évaluative (indicateurs et bases de données) afin de mesurer les enjeux liés à l’évaluation quantitative des performances scientifiques. Nous montrons au travers de cette recherche que les indicateurs quantitatifs, ne peuvent jamais être utilisés à eux seuls pour mesurer la qualité des entités de recherche étant donnée les disparités des résultats selon les périmètres d’analyse, les problèmes ex-ante liés aux caractéristiques individuelles des chercheurs qui affectent directement les indicateurs quantitatifs, ou encore les insuffisances des bases de données à partir desquelles ils sont calculés. Pour une évaluation responsable, il est impératif d’accompagner les mesures quantitatives par une appréciation qualitative des pairs. Par ailleurs, nous avons également examiné l’efficacité des mesures quantitatives pour des fins de compréhension de l’évolution des sciences et la formation des communautés scientifiques. Notre analyse appliquée sur un corpus de publications traitant la thématique de la crise économique nous a permis de montrer à la fois les auteurs et courants de pensée dominants, ainsi que l’évolution temporelle des termes utilisés dans cette thématique. / The issue of productivity and the "quality" of scientific research is one of the central issues of the 21st century in the economic and social world. Scientific research, source of innovation in all fields, is considered the key to economic development and competitiveness. Science must also contribute to the societal challenges defined in the Framework Programmes for Research and Technological Development (H2020) for example, such as health, demography and well-being. In order to rationalize public spending on research and innovation or to guide the investment strategies of funders, several indicators are developed to measure the performance of research entities. Now, no one can escape evaluation, starting with research articles, researchers, institutions and countries (Pansu, 2013, Gingras, 2016). For lack of methodological comprehension, quantitative indicators are sometimes misused by neglecting the aspects related to their method of calculation / normalization, what they represent or the inadequacies of the databases from which they are calculated. This situation may have disastrous scientific and social consequences. Our work plans to examine the tools of evaluative bibliometrics (indicators and databases) in order to measure the issues related to the quantitative evaluation of scientific performances. We show through this research that the quantitative indicators, can never be used alone to measure the quality of the research entities given the disparities of the results according to the analysis perimeters, the ex-ante problems related to the individual characteristics of researchers who directly affect the quantitative indicators, or the shortcomings of the databases from which they are calculated. For a responsible evaluation, it is imperative to accompany the quantitative measures by a qualitative assessment of the peers. In addition, we also examined the effectiveness of quantitative measures for the purpose of understanding the evolution of science and the formation of scientific communities. Our analysis, applied to a corpus of publications dealing the economic crisis, allowed us to show the dominant authors and currents of thought, as well as the temporal evolution of the terms used in this thematic.

This bibliometric study examined 19,385 citations in the bibliographies, book reviews, and reading lists of 70 volumes of Adult Education Quarterly, Adult Education, Adult Education Bulletin, Adult Education Journal, and Journal of Adult Education, and 13 volumes of the Handbook of Adult Education in the United States to identify books published before 1960 which have been frequently cited before and after 1960. Through citation analysis, an initial list of 434 titles was reduced to a core list of 64 books cited five times or more during the years between 1934 and 1988. For the purposes of this study, numbers of citations were taken to mean importance of works by indicating usefulness to subsequent authors. Of the 64 books, 55 had received at least one citation before 1960 and 57 had received at least one citation after 1960. While not all 64 of the core works constitute what might be called "classic works," it is suggested that classic works are likely to be found within the listed works.

近些年來，一些論文數據庫(特別是Libra) 變得公開化并已經開始提供給用戶他們請求獲得的信息。這使得我們可以將學術社區當做一個社會網絡來進行研究，不僅分析對作者的著作進行一些統計學的分析，還研究一個作者選擇性與其他作者合著的關係，以及一個作者對其他作者的影響。我們的研究即是定義一些基於社會網絡的方法來測試前面所說的影響關係及合著關係等。 / 我們設計的算法中，最主要的是作者影響力排名算法(AIR) 。該算法類似于著名的網頁排名算法(PageRank) ，並且把我們提取的三種關係都考慮在內。而其他的算法，都是基於某種關係或是某些關係的組合。這些算法包括:聯繫( Connection，利用合著關係、)， 追隨者數量(Follower Count ，利用發表關係)，追隨者(Follower ，利用引用關係)和平均引用數量(Balanced Citation Count ，利用合著關係和引用關係)。 / 這對這些算法，我們設計并研究了一些簡單的特例，通過算法之間橫向與縱向的比較來分析這些算法的特性。在不同的情形下，同一算法的表現并不一致，這是我們引入一個新的變量以便於靈活調整的原因。通過設定不同的變量值，我們利用距離衡量工具來度量這些算法結果的變化。 / 更進一步，我們利用不同的數據集合作為輸入來比較不同算法的表現，并利用一種距離測量工具(Spearman Footrule Distance) 來做算法之間的兩兩比較。在算法的比較中，基於排名值，我們能推斷出關於這些算法的一些結論。而基於累積值的比較，一方面驗證了這些結論的正確性，另一方面也展現出作者影響力排名算法(AIR) 的優越性。同時， 一些來源於現實生活中的排名結果，也可以用來串串證作者影響力排名算法(AIR) 的準確度。 / In recent years, some of the publications database become more publically accessible, and are starting to provide additional information users can query (this is specially the case with Libra). This allows us to study the author community as a social network, analyzing not only the statistics about papers published by an author, individually at a time, but also an author’s choice and extent in connecting to other authors (co-authoring), and an author’s influence on other authors. Our approach is todesign various social network type of metrics to measure the traits defined above. / The main algorithm Author Influence Ranking (AIR), which is analogous to PageRank algorithm, is defined by taking all three relationships into consideration. Other algorithms, based on a single relationship or combination of different relationships, include: Connection, ranking algorithm using coauthor-ship; Follower Count, ranking algorithm using the number of authors who cite papers of a particular author; Follower, ranking algorithm using citation-ship; Balanced Citation Count, ranking algorithm using citation counts normalized by coauthors. / To show properties of different algorithms and do comparison among them, we design and study primitive cases. For some algorithms, the teleportation vector leads to the deviation at certain extent. Therefore, we study different teleportation vectors by tuning a parameter β(details discussed in Chapter 4) from 0 (uniformly distributed) to 1 (proportional to authors’ productivity). With different β, we define Distance to measure the changes in results of these algorithms. / Furthermore, comparisons among these algorithms are conducted by using different publication dataset and we choose Spearman Footrule Distance in our experiment to do comparison for pair of algorithms. Rank value and cumulative value are used in the comparisons: based on the comparisons using rank value, we conclude several observations regarding these algorithms. While the comparisons based on cumulative value help us confirm the "efficiency" of AIR. For using AIR metric, we can find out those really influential researchers who may not be ranked high by other metrics. We study the influence of Turing award winners and all the Turing Award winners scored at least "B", from which we can see AIR’s "accuracy". We also apply AIR metric in the real situation. We study researchers who have Grade "A"(the grade will be discussed in Chapter 6) in Influence and find most of them have good positions in reality, which help us justify the validity of AIR.("efficiency", "accuracy" and "validity" will be discussed more in Chapter 6.) / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Song, Qianqian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 78-82). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Background --- p.2 / Chapter 1.3 --- Our Contribution and Organization --- p.5 / Chapter 2 --- Academic Ranking Algorithms --- p.8 / Chapter 2.1 --- Publication Statistics Algorithms --- p.10 / Chapter 2.1.1 --- Citation Count --- p.10 / Chapter 2.1.2 --- Balanced Citation Count --- p.11 / Chapter 2.1.3 --- Follower Count --- p.12 / Chapter 2.2 --- PageRank-like Algorithms --- p.13 / Chapter 2.2.1 --- The PageRank Algorithm --- p.13 / Chapter 2.2.2 --- Author Influence Ranking --- p.15 / Chapter 2.2.3 --- Science Author Rank Algorithm --- p.17 / Chapter 2.2.4 --- Connection --- p.20 / Chapter 2.2.5 --- Follower --- p.21 / Chapter 3 --- Analysis of Metrics Based on Primitive Cases --- p.22 / Chapter 3.1 --- The Original Case --- p.23 / Chapter 3.2 --- Case for Three General Authors --- p.24 / Chapter 3.3 --- Case for Productive Authors --- p.26 / Chapter 3.4 --- Cases for Productive Author and Coauthor-ship --- p.28 / Chapter 3.4.1 --- Type i --- p.28 / Chapter 3.4.2 --- Type ii --- p.30 / Chapter 3.5 --- Case for Coauthor-ship --- p.32 / Chapter 3.6 --- Cases for Citation Count and Balanced Citation Count --- p.34 / Chapter 3.6.1 --- Type i --- p.34 / Chapter 3.6.2 --- Type ii --- p.36 / Chapter 4 --- Key Parameter in PageRank-like Algorithms --- p.39 / Chapter 4.1 --- The Key Parameter β --- p.39 / Chapter 4.2 --- Comparison Based on β --- p.40 / Chapter 4.3 --- Discussion --- p.43 / Chapter 5 --- Algorithms Comparison --- p.46 / Chapter 5.1 --- The Description of Our Comparisons --- p.46 / Chapter 5.2 --- Similarity Between Different Metrics --- p.47 / Chapter 5.3 --- Two Dimensions Comparison --- p.51 / Chapter 5.3.1 --- Comparison in Algorithms Dimension --- p.51 / Chapter 5.3.2 --- Comparison in Time Dimension --- p.54 / Chapter 6 --- Case Study and Validation --- p.56 / Chapter 6.1 --- AIR v.s Other Metrics --- p.57 / Chapter 6.1.1 --- AIR v.s Citation Count --- p.58 / Chapter 6.1.2 --- AIR v.s Follower Count --- p.59 / Chapter 6.1.3 --- AIR v.s Follower --- p.61 / Chapter 6.1.4 --- AIR v.s Connection --- p.62 / Chapter 6.1.5 --- AIR v.s the First Active Year --- p.64 / Chapter 6.2 --- AIR v.s Rank in Reality --- p.65 / Chapter 6.2.1 --- Ranking Award Recipients --- p.65 / Chapter 6.2.2 --- Top AIR Ranking in Society --- p.65 / Chapter 7 --- Conclusion --- p.76 / Bibliography --- p.78

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Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 1996. / Includes vita and abstract. Includes bibliographical references (leaves 158-163).

Thesis (Ph.D.)--Universiteit Utrecht, 2009. / Includes bibliographical references.