Citizen satisfaction with municipal amalgamations

Abstract: When Mike Harris's Conservative government came to power in Ontario, it embarked on a program of significant municipal reform. Municipal amalgamation, one of the major elements of this reform package, had many goals, two of which were “efficient service delivery” and provision of “high‐quality services at lowest possible cost.” This article reviews three amalgamations, which reduced the number of municipalities from twenty‐nine to three, to determine whether the mergers resulted in a change in the quality of service delivery. In all three cases, there was a great outpouring of both positive and negative rhetoric in the lead‐up to the amalgamations, but not much happened. There were clearly pockets of dissatisfaction, but most residents did not see a significant change in the quality of services. Sommaire: Lorsque le gouvemement conservateur de Mike Harris est arrivé au pou‐voir en Ontario, il s'est lancé dans un programme de réformes municipales importantes. Le regroupement de municipalités, l'un des principaux éléments de cet ensemble de réformes, avait plusieurs objectifs, dont deux d'entre eux étaient une prestation efficiente de services et l'offre de services de grande qualité au prix le plus bas possible.Le présent article passe en revue trois regroupements, qui ont réduit le nombre de municipalités de vingt‐neuf à trois, pour déterminer si les fusions ont entraîné un changement dans la qualité de la prestation de services. Dans les trois cas, il y a eu une extraordinaire effusion de rhétorique, à la fois positive et négative au cours de la période précédant les regroupements, mais peu se passa. I1 y a eu de toute évidence des foyers de mécontentement, mais la plupart des résidents n'ont pas remarqué d'importants changements dans la qualité des services.     

In Profound Memory of Mr.Ikuo Hirayama

I was deeply shocked to learn of the passing of Mr.Ikuo Hirayama on December 2,2009.The sad news,like a bright star falling from the sky, brought deep grief,but also revived a succession of memories of my contacts with Mr.and Mrs.Hirayama over halfa century. The year 2007 saw the 35th anniversary of normalization of diplomatic relations between China and Japan.On June 3,I was honored to     

Intergroup Biases as a Function of Reflected Status Appraisals and Support for Legitimizing Ideologies: Evidence from the USA and Israel

Theory and research suggest that members of high-status groups feel more positively about their own group than members of low-status groups feel about their group. The studies presented here test two hypotheses derived from this general idea (1) that members of high-status groups will show greater bias in favor of the in-group when they believe that others perceive the status difference between their group and relevant low-status groups to be larger; and (2) that this relationship will be stronger when high-status group members also endorse ideologies legitimizing their privileged status. However, because low group status may have self-protective properties, it was hypothesized that imputed status differences would not relate to out-group bias among low-status group members, regardless of ideology endorsement. Two studies—using samples from the United States and Israel, respectively—provided clear support for these hypotheses. Implications for the study of both intergroup biases and legitimizing ideologies are discussed.     

Introduction: Jewish Conditions,Theories of Nationalism

Thinkers with Jewish backgrounds, whether ascribed, embraced or even denied, created the central grammar used in modern attempts to understand nationalism. This introduction first describes the central dilemma faced by those with Jewish conditions when confronted by the rise of ethnonationalism. The responses to the dilemma were varied, so the bulk of this introduction describes some of the most important intellectual boundaries—doing so in such a manner as to introduce the specialist studies that then follow.     

Structural Topic Models for Open‐Ended Survey Responses

Collection and especially analysis of open‐ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open‐ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.