1,721,074 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
A Review of Methods for Estimating Algorithmic Complexity: Options, Challenges, and New Directions
No full textSome established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account
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
Information Dynamics, Computation and Causality in Reprogramming Artificial and Biological Systems
No full textIn this talk, I will explain how algorithmic information theory, which is the mathematical theory of randomness; and algorithmic probability, which is the theory of optimal induction, can be used in molecular biology to study and steer artificial and biological systems such as genetic networks to even reveal some key properties of the cell Waddington landscape, and how these aspects help in tackling the challenge of causal discovery in science.[...
Towards Demystifying Shannon Entropy, Lossless Compression and Approaches to Statistical Machine Learning
No full textCurrent approaches in science, including most machine and deep learning methods, rely heavily at their core on traditional statistics and information theory, but these theories are known to fail to capture certain fundamental properties of data and the world related to recursive and computable phenomena, and they are ill-equipped to deal with high-level functions such as inference, abstraction, modelling and causation, being fragile and easily deceived. How is it that some of these approaches have (apparently) been successfully applied? We explore recent attempts to adopt more powerful, albeit more difficult methods, methods based on the theories of computability and algorithmic probability, which may eventually display and grasp these higher level elements of human intelligence. We propose that a fundamental question in science regarding how to find shortcuts for faster adoption of proven mathematical tools can be answered by shortening the adoption cycle and leaving behind old practices in favour of new ones. This is the case for randomness, where science continues to cling to purely statistical tools in disentangling randomness from meaning, and is stuck in a self-deluding pattern of still privileging regression and correlation despite the fact that mathematics has made important advances to better characterise randomness that have yet to be incorporated into scientific theory and practice
- …
