1,721,005 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
Conference Presentations
No full textThis repository contains a collection of posters and talk slides that I presented at conferences
Stan Model Objects
No full textThis Component contains the fitted stan model objects from the mixture-models for the project "Data, Materials, and Code for: Grouping in Working Memory Guides Chunk Formation in Long-Term Memory". The files have been separated from the main-project, to reduce the size for downloads. All results can be reproduced without the full model objects by using the extracted required parameters from the model, which are uploaded in the main project. In case you want to reproduce parameter extractions or explore the fitted models, these files can be downloaded from this component. Please consider the size of the component and the single files before downloading
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
Data and Code for: Distorting Effect of Optional Stopping with Bayes Factors are Minimal - a Commentary to Anderson et al., 2021
No full textSample size determination for robust and efficient hypothesis testing has been a longstanding issue in empirical sciences. Over the past years, Bayesian sequential designs – in which the sample size is not determined a priori but based on the evidence in the data – have become increasingly popular. While previous research has repeatedly shown the benefits of this procedure, there also has been criticism. Here, I address a critic by Anderson et al. (2021), who used simulations to show that Bayesian sequential designs lead to biased effect size estimates in the obtained data. I argue that the simulations by Anderson et al. (2021) neglect several best practice recommendations for Bayesian sequential designs, thereby exaggerating the danger of bias. Replicating the simulation design by Anderson et al. (2021), I show that biases are much smaller when following best practice recommendations (e.g., setting a minimum sample size, setting reasonable evidence thresholds, and transparently reporting studies that don’t reach an evidence threshold), and can be further mitigated by reporting Bayesian effect size estimates. Finally, I show that there is no bias when samples are submitted to a proper meta-analysis. Overall, these results support the strength of Bayesian sequential designs for an efficiently determination of sample sizes
Data, Materials, and Code for: Revisiting Hebb: The Mechanisms of Repetition Learning
No full textBack in 1961, Donald Hebb established a classic paradigm for studying repetition learning: He asked participants to remember several memory sets for an immediate serial recall task and repeated one set multiple times throughout the experiment. Participants’ ability to recall the repeated set improved gradually with repetitions, thereby demonstrating repetition learning. Explaining this effect has concerned researchers for decades, as it provides key insights into how we form durable memory representations through repeated exposure. In this review, we revisit the dominant views on the mechanisms underlying repetition learning, thereby challenging two central assumptions: The assumption that repetition learning is gradual, and the assumption that it is implicit. We show how these views have emerged from flawed analytical approaches, summarize recent evidence strongly contradicting these claims, and present a re-analysis of previously published data to illustrate how correcting implausible analytical assumptions leads to different theoretical conclusions. We propose an updated theoretical framework of the cognitive mechanisms underlying repetition learning in which we integrate elements from previous models of the Hebb repetition effect with established models of episodic memory, thereby joining two branches of the memory literature
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
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