1,721,372 research outputs found
Integrability of the Bakirov System: A Zero-Curvature Representation
Get PDFFor the Bakirov system, which is known to possess only one higher-order local generalized symmetry, we explicitly find a zero-curvature
representation containing an essential parameter
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
Analýza dopadů opatření pomocí metody Causal Forest
Get PDFRevisiting Treatment Effects with Causal Forests Aslan Bakirov Abstract This thesis focuses on the application of Causal Forests, a prominent causal machine learning algorithm, to estimate heterogeneous treatment effects in complex socio-economic phenomenon. Causal Forests leverage the capabilities of random forests to partition the high-dimensional covariate space and identify subgroups where the effect of an intervention remains constant. This approach is particularly valuable when dealing with heterogeneous causal effects, where a uniform measure of gains for all is an unrealistic assumption. Unlike traditional manual methods that are susceptible to p-hacking, the algorithm objectively uncovers nuanced treatment effect variations through data-driven analysis. The thesis demonstrates the algorithm's potential in exploring causal effects and providing valuable policy insights. An empirical illustration showcases the modeling of a complex socio-economic phenomenon, such as the gender wage gap, and leverages Causal Forests to extract policy learning from the identified heterogeneity. The study highlights the algorithm's contribution to credible and robust causal inference, bridging the gap between traditional decomposition methods and data-informed heterogeneity analysis. Keywords: Causal machine learning,...Analýza dopadů opatření pomocí metody Causal Forest Aslan Bakirov Abstrakt Tato diplomová práce se zaměřuje na aplikaci Causal Forests, prominentního algoritmu pro kauzální strojové učení, s cílem kvantifikovat heterogenní dopady různých opatření v rámci komplexních socioekonomických jevů. Předností algoritmu Causal Forest je schopnost při využití velkého množství vysvětlujících proměnných identifikovat takové podskupiny, pro které jsou efekty kauzálních jevů konstantní. Tento přístup je zvláště cenný při evaluaci dopadů takových opatření, u kterých se efekt napříč podskupinami daného vzorku výrazně liší, a u nichž je předpoklad konstantních dopadů tudíž nerealistický. Na rozdíl od tradičních manuálních metodam, které mohou být jednoduše zneužívány k tzv. p-hackingu, popsaný algoritmus objektivně identifikuje rozdíly v dopadech mezi testovanými podskupinami. Diplomová práce dále demonstruje, jak lze daný algoritmus využít k analýze heterogenních kauzálních efektů a studiu dopadů veřejných politik. Má práce pak na konkrétním příkladu gender wage gap ilustruje aplikaci algoritmu při modelování komplexních socioekonomických jevů. Studie tak jasně demonstruje výhody algoritmu při analýze dopadů opatření a zajištění vyšší robustnosti výsledků. Vyplňuje tak mezeru mezi odbornými poznatky vážících se k tradičním...CERGEFakulta sociálních vědFaculty of Social Science
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
Revisiting Treatment Effects with Causal Forests
No full textRevisiting Treatment Effects with Causal Forests Aslan Bakirov Abstract This thesis focuses on the application of Causal Forests, a prominent causal machine learning algorithm, to estimate heterogeneous treatment effects in complex socio-economic phenomenon. Causal Forests leverage the capabilities of random forests to partition the high-dimensional covariate space and identify subgroups where the effect of an intervention remains constant. This approach is particularly valuable when dealing with heterogeneous causal effects, where a uniform measure of gains for all is an unrealistic assumption. Unlike traditional manual methods that are susceptible to p-hacking, the algorithm objectively uncovers nuanced treatment effect variations through data-driven analysis. The thesis demonstrates the algorithm's potential in exploring causal effects and providing valuable policy insights. An empirical illustration showcases the modeling of a complex socio-economic phenomenon, such as the gender wage gap, and leverages Causal Forests to extract policy learning from the identified heterogeneity. The study highlights the algorithm's contribution to credible and robust causal inference, bridging the gap between traditional decomposition methods and data-informed heterogeneity analysis. Keywords: Causal machine learning,..
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
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