Institutionalization or Decoupling? An Exploratory Analysis of the UN Global Compact LEAD Initiative

Institutionalization occurs when organizations adopt policies and practices in an attempt to increase their legitimacy and competitive position. This process assumes that an organization's formal policies (macro‐level) are translated into organizational practices (micro‐level). This translation, however, can fail to occur, resulting in organizational decoupling and de‐institutionalization. Drawing on an institutionalization typology based on different levels of legitimacy (macro) and taken‐for‐grantedness (micro), the article draws on data from an assessment of the United Nations Global Compact LEAD initiative, assessing the extent to which this initiative is being institutionalized in the member firms and examining the implications for the LEAD initiative and our thinking about institutionalization.     

Hypothesis group testing for disjoint pairs

Classical group testing (CGT) is a widely applicable biotechnical technique used to identify a small number of distinguished objects from a population when the presence of any one of these distinguished objects among a group of others produces an observable result. This paper discusses a variant of CGT called group testing for disjoint pairs (GTAP). The difference between the two is that in GTDP, the distinguished items are pairs from, not individual objects in, the population. There are several biological examples of when this abstract model applies. One biological example is DNA hybridization. The presence of pairs of hybridized DNA strands can be detected in a pool of DNA strands. Another situation is the detection of binding interactions between prey and bait proteins. This paper gives a random pooling method, similar in spirit to hypothesis testing, which identifies pairs of objects from a population that collectively have an observable function. This method is simply to apply, achieves good results, is amenable to automation and can be easily modified to compensate for testing errors. M.A. Bishop is supported by AFOSR FA8750-06-C-0007. A.J. Macula is supported by NSF-0436298, AFOSR FA8750-06-C-0007.     

An Exposure‐Response Threshold for Lung Diseases and Lung Cancer Caused by Crystalline Silica

Whether crystalline silica (CS) exposure increases risk of lung cancer in humans without silicosis, and, if so, whether the exposure‐response relation has a threshold, have been much debated. Epidemiological evidence is ambiguous and conflicting. Experimental data show that high levels of CS cause lung cancer in rats, although not in other species, including mice, guinea pigs, or hamsters; but the relevance of such animal data to humans has been uncertain. This article applies recent insights into the toxicology of lung diseases caused by poorly soluble particles (PSPs), and by CS in particular, to model the exposure‐response relation between CS and risk of lung pathologies such as chronic inflammation, silicosis, fibrosis, and lung cancer. An inflammatory mode of action is described, having substantial empirical support, in which exposure increases alveolar macrophages and neutrophils in the alveolar epithelium, leading to increased reactive oxygen species (ROS) and nitrogen species (RNS), pro‐inflammatory mediators such as TNF‐alpha, and eventual damage to lung tissue and epithelial hyperplasia, resulting in fibrosis and increased lung cancer risk among silicotics. This mode of action involves several positive feedback loops. Exposures that increase the gain factors around such loops can create a disease state with elevated levels of ROS, TNF‐alpha, TGF‐beta, alveolar macrophages, and neutrophils. This mechanism implies a “tipping point” threshold for the exposure‐response relation. Applying this new model to epidemiological data, we conclude that current permissible exposure levels, on the order of 0.1 mg/m³, are probably below the threshold for triggering lung diseases in humans.     

Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.     

Case study in the Bayesian analysis of a cost‐effectiveness trial in the evaluation of health care technologies: Depression

We present a case study based on a depression study that will illustrate the use of Bayesian statistics in the economic evaluation of cost‐effectiveness data, demonstrate the benefits of the Bayesian approach (whilst honestly recognizing any deficiencies) with respect to frequentist methods, and provide details of using the methods, including computer code where appropriate. Copyright © 2003 John Wiley & Sons, Ltd.     

Diagnostics for dependence within time series extremes

Summary. The analysis of extreme values within a stationary time series entails various assumptions concerning its long- and short-range dependence. We present a range of new diagnostic tools for assessing whether these assumptions are appropriate and for identifying structure within extreme events. These tools are based on tail characteristics of joint survivor functions but can be implemented by using existing estimation methods for extremes of univariate independent and identically distributed variables. Our diagnostic aids are illustrated through theoretical examples, simulation studies and by application to rainfall and exchange rate data. On the basis of these diagnostics we can explain characteristics that are found in the observed extreme events of these series and also gain insight into the properties of events that are more extreme than those observed.     

Becoming an American and Liking It as Functions of Social Distance and Severity of Initiation

This paper examines the effects of social distance among a sample of immigrants in the process of becoming United States citizens. Using the Bogardus Social Distance Scale, a positive relationship was found between the degree of social distance and the likelihood of becoming a citizen. Social distance also serves as an important indicator of severity of initiation; the findings suggest that severity of initiation had a positive influence on reported satisfaction with life in America for those who became U. S. citizens. For those who did not become citizens, however, social distance had an inverse affect on satisfaction.     

NEGOTIATING CONTINUITY: Family and Religious Socialization for Second-Generation Asian Americans

This article examines second-generation Asian Americans' explanations of the significant influences on their own religious identities. Data include interviews (N = 73) with college-aged Asian Americans from different religious traditions and ethnic backgrounds. Respondents viewed families as the most significant influence on current religiosity. Mothers and fathers were mentioned most often, followed by extended kin, siblings, and relatives, who acted as what we call "reinforcers,""substitutes," or "contrasts" to the roles that parents played in religious socialization. These roles occurred along two religious configurations within families: heterogeneous or homogeneous degrees of religious commitment and religious affiliation. Directions for future research and contributions to the religious transmission literature follow.     

CALIBRATING MEASURES OF FAMILY ACTIVITIES BETWEEN LARGE- AND SMALL-SCALE DATA SETS

Two studies of working families are combined to demonstrate a strategy for producing reliable estimates from the combination of self-reported (large N) and observational (small N) data. Both studies examine where and how dual-career families spend time at home. The 500 Family Study is sociological and uses self-reported time diary data from a national sample; the CELF study is anthropological and uses observational scan sampling data from a regional sample of 32 families. The data are combined as if they constitute one sample, and an analytic solution for establishing the reliability of the resulting composite estimates of time use is provided. Merging the data sets provides validation for each study, neither of which is without potential methodological weaknesses. The advantages of combining data from the independent data collection methods are discussed, and selected substantive findings on families' activities are highlighted, illustrating similarities and differences between findings in the independent and combined data sets. Results show that working families spend significant time in a small spectrum of home spaces, particularly kitchens and living rooms, with leisure activities prevailing, but mothers, fathers, and children differ in where and how they spend their time. Overall, a template for merging data from different disciplines and methods is provided.