The Effects of Candidate Gender on Voting for Local Office in England

1988 ◽  
Vol 18 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Susan Welch ◽  
Donley T. Studlar

There has been considerable controversy over the reasons why women hold less than 20 per cent of all local council offices in England. Using a simple model of the votes a candidate might be expected to receive, this Note uses data from the 1985 English non-metropolitan county council elections to shed light on the paucity of women in local elected office. Our analysis evaluates the following alternative explanations for the low proportions of women in local office:1. Relatively few women are selected by parties to run for local office;2. Parties tend to nominate their women candidates for unwinnable races;3. Voters disproportionately vote against women candidates.

2021 ◽  
Vol 54 (5) ◽  
pp. 1-38
Author(s):  
Arwa I. Alhussain ◽  
Aqil M. Azmi

Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.


2003 ◽  
Vol 19 ◽  
pp. 11-23 ◽  
Author(s):  
R. I. Brafman ◽  
M. Tennenholtz

In common-interest stochastic games all players receive an identical payoff. Players participating in such games must learn to coordinate with each other in order to receive the highest-possible value. A number of reinforcement learning algorithms have been proposed for this problem, and some have been shown to converge to good solutions in the limit. In this paper we show that using very simple model-based algorithms, much better (i.e., polynomial) convergence rates can be attained. Moreover, our model-based algorithms are guaranteed to converge to the optimal value, unlike many of the existing algorithms.


Author(s):  
Rosalyn Cooperman

Voter support for women candidates in American politics may best be summed up by the often-repeated phrase, “when women run, women win.” This statement indicates that when compared to male candidates running in a similar capacity, such as candidates for open seats in which no incumbent is present, female candidates are equally likely to win elected office. Voters, therefore, seem equally likely at face value to support female candidates. However, the literature on voter support for women candidates suggests that this voter support may be more conditional in nature. A central research thread on voters and women candidates is how voters perceive women candidates and, in turn, their electability. Research on gender stereotypes and candidates examines voter perceptions of the traits they typically associate with men and women, candidates, and officeholders and the circumstances under which these traits make gender and political candidacy more or less attractive. The literature on political party and voter support for women candidates explores how gender and party affect levels of voter support and is offered as one explanation for the party imbalance in women’s representation with female Democrats significantly outnumbering female Republicans as candidates and officeholders. Researchers have also examined how voters evaluate other components of women’s candidacies, including their party affiliation, race, ethnicity, and sexual orientation. In addition to personal characteristics, scholars have explored how the type or level of office impacts voter support of women candidates with certain types of elected positions often considered more or less well suited for women candidates. More recently, a thread of research on voter support for women candidates has focused on women’s absence from the nation’s highest elected position—the US presidency. Scholars, and the candidate herself, have assessed voter support for or opposition to Hillary Clinton’s unsuccessful presidential bids in 2008 and 2016. This line of research includes public opinion polling that measures both the abstract idea of electing a woman president as well as electing a specific woman president, namely Clinton.


2018 ◽  
Author(s):  
Tadeg Quillien

Why would people hide positive information about themselves? Evolutionary game theorists have recently developed the signal-burying game as a simple model to shed light on this puzzle; they have shown that the game has an equilibrium where some agents are better off deliberately reducing the visibility of the signal by which they broadcast their positive traits. However, their explanation falls short of explaining all modesty norms, since this equilibrium also features individuals who openly brag. This leaves modesty norms that everyone adheres to in want of an explanation. Here we show that the signal-burying framework actually affords such an explanation: the game contains an equilibrium where all agents who send a signal voluntarily reduce its conspicuousness. Surprisingly, the stability of the two kinds of equilibria rely on very different principles. The equilibrium where some agents brag is stable because of costly signaling dynamics. By contrast, the universal modesty equilibrium exists because buried signals contain probabilistic information about a sender's type, and receivers make optimal use of this information. In the latter equilibrium, burying a signal can be understood as a handicap which makes the signal more honest, but honesty is not achieved through standard costly signaling dynamics.


Author(s):  
Fergus Millar

This epilogue examines various strands of social history, religious affiliation and language in the Roman Near East in relation to the beginning of Muhammad's preaching in about 610. Muhammad was born, probably in about 570, in Mecca, where he began to receive divinely inspired messages in Arabic. After he died, Muhammad's followers invaded the nearest Roman provinces and conquered all of the Roman Near East, the Sasanid empire, Egypt and Roman North Africa. These are known as ‘the great Arab conquests’. This chapter considers whether the Arabian Peninsula can be properly treated under the title of ‘Arabia and the Arabs’. It also analyses evidence from the Mediterranean and Mesopotamian Near East, as well as the kingdom of Himyar. Finally, it looks at brief allusions to the life-history of Muhammad in a number of Christian sources to shed light on his preaching.


2019 ◽  
Vol 35 (2) ◽  
pp. 353-386 ◽  
Author(s):  
Jennifer Dykema ◽  
Dana Garbarski ◽  
Ian F. Wall ◽  
Dorothy Farrar Edwards

Abstract While scales measuring subjective constructs historically rely on agree-disagree (AD) questions, recent research demonstrates that construct-specific (CS) questions clarify underlying response dimensions that AD questions leave implicit and CS questions often yield higher measures of data quality. Given acknowledged issues with AD questions and certain established advantages of CS items, the evidence for the superiority of CS questions is more mixed than one might expect. We build on previous investigations by using cognitive interviewing to deepen understanding of AD and CS response processing and potential sources of measurement error. We randomized 64 participants to receive an AD or CS version of a scale measuring trust in medical researchers. We examine several indicators of data quality and cognitive response processing including: reliability, concurrent validity, recency, response latencies, and indicators of response processing difficulties (e.g., uncodable answers). Overall, results indicate reliability is higher for the AD scale, neither scale is more valid, and the CS scale is more susceptible to recency effects for certain questions. Results for response latencies and behavioral indicators provide evidence that the CS questions promote deeper processing. Qualitative analysis reveals five sources of difficulties with response processing that shed light on under-examined reasons why AD and CS questions can produce different results, with CS not always yielding higher measures of data quality than AD.


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