1,721,080 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
Detection and formal classification of certainty and its application to text mining of chains of scholarly statements
No full textLas estructuras gramaticales que los investigadores usan para expresar sus afirmaciones intentan transmitir diversos grados de certeza o especulación. Estudios anteriores han sugerido una variedad de sistemas de categorización para la certeza académica; sin embargo, estos no han sido validados objetivamente, particularmente con respecto a representar la interpretación del lector, en lugar de la intención del autor. En esta tesis intentamos un enfoque de categorización de certeza basado en un modelo de datos. Ejecutamos una serie de estudios basados en cuestionarios utilizando frases académicas seleccionadas manualmente, en inglés, para determinar cómo los investigadores clasifican varias afirmaciones académicas, utilizando tres sistemas distintos de clasificación de certeza. Luego intentamos definir objetivamente las categorías de certeza percibida entre los lectores de textos biomédicos mediante el examen del grado de acuerdo/consistencia en la selección de categorías de certeza, por parte de los mismos lectores. Posteriormente se aplican pruebas estadísticas para evaluar el grado en que el sistema de categorización proporcionado en cada encuesta refleja la percepción de aquellos a quienes se les pidió que usaran esas categorías. El sistema de categorización con la puntuación más alta, es decir, el que proporcionó el nivel más alto de acuerdo, se usó para crear manualmente un gran corpus de declaraciones anotadas con su respectivo grado de certeza. Esto, a su vez, se utilizó para generar un modelo de “machine-learning” capaz de clasificar automáticamente nuevas declaraciones entre estas categorías, con alta precisión. Proponemos que este modelo podría usarse dentro de los algoritmos existentes de minería de texto para capturar metadatos adicionales que reflejen la expresión de certeza en el texto original. Además, proporcionamos un ejemplo de una publicación académica “machine-accesible", una Nanopublicación, en la que hemos incorporado estos nuevos metadatos de certeza contextual. Descubrimos que los lectores perciben tres categorías de certeza: un nivel de certeza alta y dos niveles de certeza más baja que están menos diferenciados, pero que muestran un grado significativo de acuerdo entre lectores. Mostramos que estas categorías se pueden detectar de manera automatizada, utilizando un modelo de “machine-learning”, con una precisión de “Cross-Validation” (CV) del 89,0% en relación a un "gold-standard" generado manualmente, y una precisión del 82,2% contra un corpus formado por las respuestas de los lectores. Este hallazgo brinda la oportunidad de capturar metadatos contextuales relacionados con la certeza como parte de proyectos de minería de texto, que actualmente omiten estas sutiles claves lingüísticas. Proporcionamos como ejemplo un conjunto de nanopublicaciones “machine-accesible” que representan todas las declaraciones analizadas en esta tesis, donde la categoría de certeza asignada por nuestro modelo de aprendizaje automático está integrada como metadatos de manera formal con base ontológica como prueba de concepto. ----------ABSTRACT---------- The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation of the reader, rather than the intention of the author. In this thesis we attempt a data-driven certainty categorization approach. We execute a series of questionnaire-based studies using manually-curated scholarly assertions, in English, to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We then attempt to objectively define categories of perceived certainty that are shared among readers of biomedical text by examining the degree of consistency in certainty category selection by these readers. Statistical tests are then applied to evaluate the degree to which the categorization system provided in each Survey reflects the perception of those asked to use those categories. The categorization system with the highest score - that is, the one that provided the highest level of agreement - was then used to manually create a large corpus of certainty-annotated statements. This, in turn, was used to generate a machine-learning model capable of automatically classifying new statements into these categories with high accuracy. We propose that this model could be used within existing text-mining algorithms to capture additional metadata reflecting the nuanced expression of certainty in the original text. Additionally, we provide an example of a machine-accessible scholarly publication - a NanoPublication - within which we have embedded this novel contextual certainty metadata. We found that there are three categories of certainty perceived by readers: one level of high certainty, and two levels of lower certainty that are somewhat less distinct, but nevertheless show a significant degree of inter-annotator agreement. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-validation (CV) accuracy of 89,0% relative to a manually-generated “gold standard”, and 82,2% accuracy against a publicly-annotated corpus. This finding provides an opportunity for contextual metadata related to certainty to be captured as a part of text-mining pipelines, which currently miss these subtle linguistic cues. We provide a set of exemplar machine-accessible Nanopublications representing all statements analyzed in this thesis, where the certainty category assigned by our machine learning model is embedded as metadata in a formal, ontology-based manner as proof-of-concept
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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