Interaction ism and the Child: Cahill,Corsaro, and Denzin on Childhood Socialization

Socialization is one of the defining concepts of symbolic interactionism. This essay redirects attention to that area by reviewing the works of Spencer E, Cahill, William A. Corsaro, and Norman K. Denzin, three scholars who have had little attention paid to their work on childhood socialization. Under the rubrics of (a) stages: automatic or contingent?, (b) play and games, (c) peer groups and cultures, (d) gender identity, and (e) public behavior, it is argued that these scholars have advanced symbolic interaction theory on childhood socialization by featuring the constructive, communal, and collective aspects of socialization. A summary of similarities and differences among these authors is made, especially noting Corsaro's conceptualization of “interpretive reproduction.” A critique focuses on some inadequacies in their work along with suggestions for future research.     

The ideological context of child welfare

This paper examines consequences for child development and well-being of the prevailing institutional order and ideology of the United States and concludes that child development in our country is severely obstructed because of socially structured violence which permeates our way of life. The paper ends with comments on prospects for social transformation which seems necessary to assure the development and well-being of every child.     

An operative extension of the likelihood ratio test from fuzzy data

In the present paper we are going to extend the likelihood ratio test to the case in which the available experimental information involves fuzzy imprecision (more precisely, the observable events associated with the random experiment concerning the test may be characterized as fuzzy subsets of the sample space, as intended by Zadeh, 1965). In addition, we will approximate the immediate intractable extension, which is based on Zadeh’s probabilistic definition, by using the minimum inaccuracy principle of estimation from fuzzy data, that has been introduced in previous papers as an operative extension of the maximum likelihood method.     

Review Essay: Democratic Government in the Age of Globalization?

Public Organization Review -     

Unbiased estimation of income inequality

In a previous paper Gastwirth shows that a broad family of measures of inequality can be accurately estimated when the tax data are known in groups (more precisely, when we know the number of returns in each of several class intervals and their corresponding average income). In the present paper we show that some measures of the preceding family can be unbiasedly estimated when the tax data are individually known for a sample from the population. Specifically, we construct unbiased estimators of a particular measure of inequality in the samplings with and without replacement, and in the stratified samplings with and without replacement.     

A note on stratification and gain in precision in estimating diversity from large samples

Several indices of entropy have been suggested in the literature as weighted diversity measures of a population with respect to a classification process. Among them, Shannon's entropy and Havrda -Charvát's non-additive entropies of order a, have been exhaustively used.

When the population is finite but too large to be censused, the diversity with respect to a given classification process must be estimated from a sample.

In this note, on the basis of an asymptotic study of the sample indices in the stratified random sampling, we are going to confirm that when we deal with large samples one can guarantee a gain in precision from stratified random over simple random sampling. This gain becomes considerable when the ‘inaccuracy" (as intended by Kerridge and Rathie and Kannapan) between the frequency vector in each stratum and that in the whole population, varies greatly from stratum to stratum.     

On the estimation of the true probability of a correct selection

For ranking and selection problems, the true probabiIity of a correct selection P(CS) is unknown even if a selection is made under the indifference-zone approach. Thus to estimate the true P(CS) some Bayes estimators and a bootstrap estimator are proposed for two normcal populations with common known variance. Also a bootstrap estimator and a bootstrap confidence interval are proposed for normal populations with common unknown variance. Some comparisons between proposed estimators and some other known estimators are made via Monte Carlo simulations.     

Avian biotic homogenization driven by airport-affected environments

Urban Ecosystems - Building and operating airports are human activities associated with adverse changes in the natural environment, resulting in threats to the biodiversity in airport surroundings....     

The rise of randomized controlled trials (RCTs) in international development in historical perspective

Theory and Society - This article brings a historical perspective to explain the recent dissemination of randomized controlled trials (RCTs) as the new “gold standard” method to assess...