The United States is undergoing dramatic demographic change, primarily from immigration, and many of the new Latino immigrants are settling in the South. This paper examines hypotheses related to attitudes of Latino immigrants toward black Americans in a Southern city. The analyses are based on a survey of black, white, and Latino residents (n = 500). The results show, for the most part, Latino immigrants hold negative stereotypical views of blacks and feel that they have more in common with whites than with blacks. Yet, whites do not reciprocate in their feelings toward Latinos. Latinos' negative attitudes toward blacks, however, are modulated by a sense of linked fate with other Latinos. This research is important because the South still contains the largest population of African Americans in the United States, and no section of the country has been more rigidly defined along a black-white racial divide. How these new Latino immigrants situate themselves vis-à-vis black Americans has profound implications for the social and political fabric of the South.

The use of the proximity model to represent the relationship between citizens' policy attitudes and the positions of candidates on the issues of the day has considerable appeal because it offers a bridge between theoretical models of political behavior and empirical work. However, there is little consensus among applied researchers about the appropriate representation of voter behavior with respect to the measurement of issue distance, candidate location, or whether to allow heterogeneity in the weight that each individual places on particular issues. Each of these choices suggests a different, and reasonably complicated, nonlinear relationship between voter utility and candidate and voter issue positions which may have a meaningful influence on the substantive conclusions drawn by the researcher. Yet, little attention has been given to the best way to represent the proximity model in applied work. The purpose of this paper is to identify which forms of the proximity model work best, with particular consideration given to the identification of functional forms that are invariant to the choice of scale for the independent variables.

Dramatic demographic changes are occurring in the United States, and some of the most dramatic changes are occurring in the South from Latino immigration. Latinos, by and large, are an entirely new population in the region. How are Black southerners reacting to this new population? Using survey data gathered from a southern location, this article explores several questions related to whether Blacks see these new residents as friendly neighbors or economic competitors. Results suggest that Blacks and non-Blacks perceive a potential economic threat from continued Latino immigration, but Blacks are more concerned about the effects of Latino immigration than are Whites.

B eck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and modelbuilding implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses. D uring the 1990s, quantitative security studies became an increasingly prominent and sophisticated area of inquiry within our discipline. Just in American Political Science Review, more than 20 articles over the past five years have applied some form of econometric technique to the study of international conflict. In particular, estimators based on the general linear model have been central to the development of extensive literatures on deterrence, the impact of democracy and trade on international conflict, and many other issues. In the March 2000 issue of this Review (Vol. 94, No. l, 21-36) Beck, King, and Zeng (hereafter, BKZ) offered a sweeping critique of these research programs and a bold new direction for future research. They contend that standard parametric procedures are not up to the task of estimating the causes of international conflict because these relationships are "highly non-linear, massively interactive, and heavily context dependent or contingent" (22). 1 Consequently, improvements in theory and data are for naught, without a substitute for the inadequate, inflexible models based on the general linear model. As an alternative, BKZ introduce a statistical estimator, commonly called a "neural network," that can approximate complex relationships without prior assumptions.The evidence for BKZ's claim is the alleged ability of neural networks to improve forecasts of the onset of militarized disputes. We are, however, less sanguine about the forecasting performance of neural networks compared to more traditional logit models. If the goal is to reject an entire research paradigm, it seems appropriate to use the best model that paradigm has to offer. But we contend that BKZ omitted several variables and

Previous research has suggested that party labels operate like brand names that citizens use to inform their votes. The article argues that this earlier work has focused too much on the content of party messages, while the informational value of party labels also depends on voter uncertainty about the party's behavior in office. These intuitions are developed in a Bayesian learning model in which voters update their beliefs about the mean and variance in the distribution of parties' ideologies, and apply these beliefs to a spatial model of partisan choice with risk-averse voters. The model predicts that if party unity is high, then party labels will provide a useful signal to voters about candidate characteristics and identifications with the parties will be strong, but if party unity is low, then party attachments will be weak. This approach seems to explain both the stability in respondents' political preferences over the life-cycle and the decline and resurgence in the strength of party identifications in the American electorate over the last half-century.