Knowledge,boundaries, and bodies: Social construction between medical sociology and science and technology studies

Medical sociology and science and technology studies (STS) emerged from different positions, but often closely related concerns, within the broad discipline of sociology. Their interface and areas of overlap have mostly been shaped by theoretical positions broadly considered “social constructionist.” Taken together, these perspectives provide empirical and theoretical tools to analyze important questions about how social inequalities, forms of scientific knowledge, and patterns of human health come to be produced and feedback into one another. Examining their intersection enables sociological questions such as: How is medical and public health scientific knowledge produced, stabilized, and taken as fact? How are scientific facts about health and illness used, experienced, and challenged? What is the relationship between health inequalities and public health or medical knowledge? This article seeks to briefly trace the important contributions that social constructionist research has made at the interstices of medical sociology and STS, further clarifying the history, points of intersection, and areas of diversion between them. The current COVID-19 pandemic has unveiled the political struggles that constitute public health scientific knowledge and circulation. The interface between STS and medical sociology can help us to make sense of the interrelationships between politics, power inequalities, and public health scientific knowledge.     

Many-to-one comparison of nonlinear growth curves for Washington's Red Delicious apple

In this article, we are interested in comparing growth curves for the Red Delicious apple in several locations to that of a reference site. Although such multiple comparisons are common for linear models, statistical techniques for nonlinear models are not prolific. We theoretically derive a test statistic, considering the issues of sample size and design points. Under equal sample sizes and same design points, our test statistic is based on the maximum of an equi-correlated multivariate chi-square distribution. Under unequal sample sizes and design points, we derive a general correlation structure, and then utilize the multivariate normal distribution to numerically compute critical points for the maximum of the multivariate chi-square. We apply this statistical technique to compare the growth of Red Delicious apples at six locations to a reference site in the state of Washington in 2009. Finally, we perform simulations to verify the performance of our proposed procedure for Type I error and marginal power. Our proposed method performs well in regard to both.     

Community Organization and the Oppressed

There is sexist bias in social work education, as teaching materials for the courses Human Growth and Development and Family Casework exemplify. Two leading authorities on adolescence, for example, accept a stereotypic view of female development. Family treatment writers reveal their ambivalence about male dominance within family structures, as they invoke such concepts as the “male head of the house,” and “sex role reversal,” while simultaniously encouraging independent development of each partner. In addition, the Women's Liberation Movement has created complex reactions among student practitioners.     

The Gauss—Markov Theorem and Random Regressors

In the standard linear regression model with independent, homoscedastic errors, the Gauss—Markov theorem asserts that = (X'X)^-1(X'y) is the best linear unbiased estimator of β and, furthermore, that is the best linear unbiased estimator of c'β for all p × 1 vectors c. In the corresponding random regressor model, X is a random sample of size n from a p-variate distribution. If attention is restricted to linear estimators of c'β that are conditionally unbiased, given X, the Gauss—Markov theorem applies. If, however, the estimator is required only to be unconditionally unbiased, the Gauss—Markov theorem may or may not hold, depending on what is known about the distribution of X. The results generalize to the case in which X is a random sample without replacement from a finite population.