1,721,266 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
Piper kadsura
No full textCountry= The Netherlands
Comments= Caulis Piperis Kadsurae (Hai Feng Teng)
Common names= Japanese pepper
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
Degree of Risk Aversion and Demand for Insurance of Households in the Presence of Background Risk
No full text本論文分為兩部分，第一篇研究利用Halek and Eisenhauer (2001)之研究中所提出利用壽險資料推導出Arrow-Pratt風險趨避之縮減式，並以台灣家庭收支調查資料為樣本，實證估計家計單位的風險趨避係數（包括絕對風險趨避係數及相對風險趨避係數），估計結果與Halek and Eisenhauer (2001)之估計類似，相對風險趨避係數分配為右偏，多數集中在0至4之間。研究進一步探討背景風險是否為影響風險趨避程度高低之因素，背景風險定義為獨立於其他風險之不可投保風險，所得風險是最常用來代理背景風險的變數，本研究以所得變異係數來代表家計單位無法透過保險或其他避險方法來控制的背景風險，迴歸模型並加入了其他解釋變數包括家庭所得及資產等財富變數、戶長及家庭特性等社會變數及其他地理變數。主要實證結果顯示所得風險愈高其風險趨避程度亦愈高，表示樣本家計單位偏好符合Pratt and Zeckhauser (1987)之適當風險趨避(proper risk aversion)， Kimball (1993)之標準風險趨避(standard risk aversion)，和Gollier and Pratt (1996)之風險脆弱性(risk vulnerability)等理論之充分條件及必要條件。二篇研究主要探討家計單位保險（包括人壽醫療險及產物保險）之購買決策、消費比例及支出金額是否受到背景風險（以所得變異係數及標準差及減薪廠商比例等所得風險變數作為代理變數）之影響，並實證估計各種保險之所得彈性。利用不同之迴歸模型（包括Logistic、 Tobit及OLS等方法）並控制相關解釋變數包括財富變數及其他社會及地理變數後，實證結果顯示所得風險會正向影響家計單位的保險需求，面對愈高所得風險的家庭會有較高機率購買保險以及傾向購買較多保險，此結果與Guiso and Jappelli (1998)和Koeniger (2004)分別針對責任險及汽車險所做之實證分析結果相同，亦與Eeckhoudt and Kimball (1992)和 Schlesinger (1999)推導出之理論模型一致，家計單位偏好符合Pratt and Zeckhauser (1987)、Kimball (1993)和Gollier and Pratt (1996)之條件。研究並估計保險之所得彈性，實證結果顯示所得彈性為正，表示保險購買支出會隨著所得提高而增加，代表保險屬於正常財，此結論與其他相關保險實證研究結果一致。Essay 1 of this study uses life insurance expenditure data of Survey of Family Income and Expenditure (SFIE) in Taiwan to estimate the Arrow-Pratt risk aversion coefficient of households empirically by using the reduced form equation derived by Halek and Eisenhauer (2001). This study provides empirical evidence on the nature of the relationship between the risk aversion and background risk which is not under the control of the agent, and that is independent of endogenous risks. Using the coefficient variation of household income as the proxy for background risk, after controlling other factors including household income and wealth, the characteristics of the head of household and other demographic variables, the results suggest that households which are more likely to face higher income risk exhibit a greater coefficient of risk aversion. This finding is consistent with consumer preferences being characterized by proper risk aversion (Pratt and Zeckhauser, 1987), standard risk aversion (Kimball, 1993) and risk vulnerability (Gollier and Pratt, 1996) which are the necessary and sufficient conditions of the optimal risk-taking behavior in the presence of background risk.ssay 2 of this study investigates how background risk affects households’ insurance purchasing decision, expenditure share and amounts of insurance by using data of Survey of Family Income and Expenditure (SFIE) in Taiwan. Using the income risk as the proxy for background risk and controlling other wealth and demographic factors, the findings suggest that insurance expenditure is positively affected by uninsurable background risk. This results suggest that consumer with more income risk is more risk averse and leads a higher demand of insurance. This finding is similar to the empirical results of Guiso and Jappelli (1998) and Koeniger (2004) and is consistent with the theory models derived by Eeckhoudt and Kimball (1992) and Schlesinger (1999). This finding is also consistent with consumer preferences being characterized by proper risk aversion, standard risk aversion and risk vulnerability. This study also finds that the coefficient income elasticity of insurance is positive that means people tend to increase insurance expenditure with respect to an increase in income. This result is consistent with most empirical studies of insurance demand that suggest that a consumer’s income change has positive effect on the consumer’s demand for insurance and suggest that insurance is a normal good.Essay I. The Relationship between Degree of Risk Aversion and Background Risk 1. Introduction 1. Theory models 5.1 Arrow-Pratt Risk Aversion Coefficient 5.2 Risk Aversion and Background Risk 6. Data and Empirical Results 8.1 Coefficients of Risk Aversion 8.2 Regression Results 9. Conclusions 18ssay II. Households′ Demand for Insurance in the Presence of Background Risk 19. Introduction 19. The Model for Demand for Insurance with Background Risk 23. Empirical Models 26. Data and Empirical Results 30.1. Data and Sample 31.2. Empirical Results 35. Conclusion 46eferences 48ppendix: Definition of variables 5
- …
