1,720,954 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
The use of Artificial Intelligence as a tool for programming students
No full textArtificial intelligence is an emerging technology with constantly expanding applications. Asthe technology evolves, us humans are becoming more adept at utilizing this new tool. Research investigating the use of artificial intelligence indicates an increase in productivity among its users. However, how have students adapted to this tool? In this study, we examined the use of artificial intelligence among student programmers. We collected and analyzed data on the frequency, area of use and extent of AI usage through a survey completed by 90 programming students. The responses we received came from three types of student programmers: system developers, frontend web developers and full-stack web developers. The results show that artificial intelligence is primarily used for code explanation, as a source of inspiration, for correcting erroneous code, and for bug fixing. Web developers use artificial intelligence for approximately one-third of their assignments. Half of these students use artificial intelligence on half, or more than half, of the days they actively study programming. System developers use artificial intelligence for slightly more than half of their assignments and approximately three-quarters of them use artificial intelligence on half, or more than half, of the days they study. Web developers delegate slightly less than one-fifth of the work per assignment to artificial intelligence, while system developers, on average, delegate 42.5% of the work to artificial intelligence.Artificiell intelligens är en ny teknik vars användningsområden ständigt växer. Samtidigt som tekniken utvecklas blir vi människor även bättre på att använda detta nya verktyg. Forskning som undersökt användningen av artificiell intelligens visar på en ökning i produktivitet bland dem som använder det. Men hur har studenter hunnit anpassa sig till detta verktyg? Vi har i denna studie undersökt användningen av artificiell intelligens bland studerande programmerare. Vi har samlat in och analyserat hur ofta det används, till vad det används samt hur mycket det används. Detta har gjorts via en enkätundersökning som 90 studenter som studerar programmering har svarat på. De svar vi fått in kommer från tre sorters studerande programmerare: systemutvecklare, frontend webbutvecklare och fullstack webbutvecklare. Resultatet visar att artificiell intelligens mestadels används till att förklara kod, som inspirationskälla, att korrigera felaktig kod och till buggfixing. Webbutvecklarna använder artificiell intelligens till ungefär en tredjedel av sina uppgifter, och hälften av dessa studenter använder artificiell intelligens hälften, eller fler, av de dagar de aktivt studerar programmering. Systemutvecklarna använder artificiell intelligens till lite mer än hälften av sina uppgifter och ungefär tre fjärdedelar av systemutvecklarna använder artificiell intelligens under hälften, eller fler, av de dagar som de studerar. Webbutvecklarna låter artificiell intelligens göra lite mindre än en femtedel av arbetet per uppgift medan systemutvecklarna i genomsnitt låter artificiell intelligens göra 42,5% av arbete
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
The use of Artificial Intelligence as a tool for programming students
No full textArtificial intelligence is an emerging technology with constantly expanding applications. Asthe technology evolves, us humans are becoming more adept at utilizing this new tool. Research investigating the use of artificial intelligence indicates an increase in productivity among its users. However, how have students adapted to this tool? In this study, we examined the use of artificial intelligence among student programmers. We collected and analyzed data on the frequency, area of use and extent of AI usage through a survey completed by 90 programming students. The responses we received came from three types of student programmers: system developers, frontend web developers and full-stack web developers. The results show that artificial intelligence is primarily used for code explanation, as a source of inspiration, for correcting erroneous code, and for bug fixing. Web developers use artificial intelligence for approximately one-third of their assignments. Half of these students use artificial intelligence on half, or more than half, of the days they actively study programming. System developers use artificial intelligence for slightly more than half of their assignments and approximately three-quarters of them use artificial intelligence on half, or more than half, of the days they study. Web developers delegate slightly less than one-fifth of the work per assignment to artificial intelligence, while system developers, on average, delegate 42.5% of the work to artificial intelligence.Artificiell intelligens är en ny teknik vars användningsområden ständigt växer. Samtidigt som tekniken utvecklas blir vi människor även bättre på att använda detta nya verktyg. Forskning som undersökt användningen av artificiell intelligens visar på en ökning i produktivitet bland dem som använder det. Men hur har studenter hunnit anpassa sig till detta verktyg? Vi har i denna studie undersökt användningen av artificiell intelligens bland studerande programmerare. Vi har samlat in och analyserat hur ofta det används, till vad det används samt hur mycket det används. Detta har gjorts via en enkätundersökning som 90 studenter som studerar programmering har svarat på. De svar vi fått in kommer från tre sorters studerande programmerare: systemutvecklare, frontend webbutvecklare och fullstack webbutvecklare. Resultatet visar att artificiell intelligens mestadels används till att förklara kod, som inspirationskälla, att korrigera felaktig kod och till buggfixing. Webbutvecklarna använder artificiell intelligens till ungefär en tredjedel av sina uppgifter, och hälften av dessa studenter använder artificiell intelligens hälften, eller fler, av de dagar de aktivt studerar programmering. Systemutvecklarna använder artificiell intelligens till lite mer än hälften av sina uppgifter och ungefär tre fjärdedelar av systemutvecklarna använder artificiell intelligens under hälften, eller fler, av de dagar som de studerar. Webbutvecklarna låter artificiell intelligens göra lite mindre än en femtedel av arbetet per uppgift medan systemutvecklarna i genomsnitt låter artificiell intelligens göra 42,5% av arbete
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