Effect of Brief Staff‐Assisted Career Service Delivery on Drop‐In Clients

Brief staff‐assisted career counseling services based on cognitive information processing theory were evaluated using 138 drop‐in clients who completed pre‐ and postintervention self‐report measures of knowledge about next steps, confidence in making next steps, and anxiety about career concerns. Significant changes occurred in each dimension and were significantly associated with the quality of career adviser interaction. Participants rated the experience as highly positive. Decreases in anxiety about career concerns were unrelated to the intervention. Implications include that a brief service delivery model yields positive outcomes for many clients but requires adjustments in organizational culture, physical environment, practitioner approaches, and client expectations. Future research should examine relational variables related to decreased anxiety with a brief service delivery model, the degree to which this approach works with specific career needs, and client attributes leading to successful versus unsuccessful outcomes with this model.     

Education, Ability, and Civic Engagement in the Contemporary United States

Participation in civic activities is widely recognized as the necessary foundation for any democratic government. Education has long been identified as a key determining factor of such civic engagement. Occasionally, this traditional finding has been called into question. Some scholars have hypothesized, while others firmly asserted, that cognitive ability fills the role usually attributed to education. This hypothesis implies that intelligence determines participation and, consequently, good citizenship. Thus, an inquiry into the relationships among education, cognitive ability, and civic participation raises important questions about citizenship in a democracy. By examining data from the 1976 American National Election Study (ANES), the 1974–1990 General Social Surveys (GSS), and the 1957–1975 Wisconsin Longitudinal Study (WLS), I test the hypothesis that education is only a proxy for cognitive ability. I find that analysis using the ANES, which includes a very poor ability measure, produces inconclusive evidence. Yet analysis of data that include better measures of ability, the GSS and the WLS, rejects this hypothesis. In conclusion, the assertion of ability's dominance in determining civic participation is empirically untenable.     

Survival,abundance, and capture rate of eastern cottontail rabbits in an urban park

Eastern cottontail rabbits (Sylvilagus floridanus) are common, conspicuous denizens of urban environments. They are associated with human-wildlife conflict due to vegetation damage. Prior to this study, population dynamics of this species in urban environments remained largely uncharacterized. For three consecutive winters, we used classic field ecology methods (mark-recapture and mark–resight surveys) to estimate demographic parameters of rabbits in a city park in Chicago, Illinois. Rabbits occurred in densities as high as 16.3 rabbits/ha, which is comparatively high for the Midwestern United States. An annual survivorship of 30.4?±?12.9 % SE was similar to that observed in natural environments in similar climates. This result refuted our hypothesis that urban rabbits would have higher annual survival rates than rabbits in natural settings due to food subsidies supplied by landscaping in parks. Mean distance between trap locations for rabbits trapped three or more times was 43.14?±?30.01 m SD, suggesting that rabbits in the urban study area had smaller home ranges than rabbits in non-human-dominated habitats. This study contributes to our understanding of population dynamics of a human-wildlife conflict species in urban environments and provides useful information for managers dealing with damage caused by rabbits. The mark-resight method employed here could be used by managers to estimate pre- and post-management population sizes of other conflict species, for example Canada geese (Branta Canadensis), in parks and green spaces, provided that the species is trappable, visible, and individuals have relatively small home ranges.     

Oil,Polity, and Civil Society: The Construction of a Hegemonic System in Iraq (1945–1958)

Conventional theories of political economy assume that dependency on natural resources, such as oil, have a negative impact on the development of a democratic political system. While there is a developed body of work that explains the different mechanisms that shape this relationship, one mechanism, the role of the civil society, remains mostly ungrasped. As a result, the aim of this paper is to expand our understanding of the group formation effect, first discussed by Michael Ross (2001), and unpack the mechanisms that underline it, by looking at the relationship between rent from oil production, the civil society, and the rise of authoritarianism in Iraq during the period of 1945–1958. We argue that examining the relationship between these variables will help us understand the past and the current situation in Iraq, and it will improve our knowledge of the political formations and activism in oil‐producing countries in general.     

Adversarial Risk Analysis with Incomplete Information: A Level‐k Approach

This article proposes, develops, and illustrates the application of level‐k game theory to adversarial risk analysis. Level‐k reasoning, which assumes that players play strategically but have bounded rationality, is useful for operationalizing a Bayesian approach to adversarial risk analysis. It can be applied in a broad class of settings, including settings with asynchronous play and partial but incomplete revelation of early moves. Its computational and elicitation requirements are modest. We illustrate the approach with an application to a simple defend‐attack model in which the defender's countermeasures are revealed with a probability less than one to the attacker before he decides on how or whether to attack.     

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis.     

What are We Working For? Comparing the Importance of Job Features for Job Satisfaction over the Career Span

This study examines the impact, relative importance, and unique predictive validity of changes in a host of job features and other relevant factors on job satisfaction over a 35-year period using data from the 1979 National Longitudinal Survey of Youth cohort (12,686 respondents). The design of our analysis eliminated of the impact of established between-person correlates of job satisfaction (e.g., personality, cognitive ability), thereby providing for direct examination of within-person change in the focal variables. Results using a first-difference regression and a relative weights analysis revealed that changes in intrinsic, social, and extrinsic features corresponded to changes in satisfaction, but with differing magnitudes. Specific features (e.g., different dimensions of the Job Characteristics Model) generally were unique predictors when all variables were considered. Findings regarding the importance of various features substantially deviate from employees’ reports about contributors to job satisfaction and, in some cases, also differ from findings drawn from cross-sectional data.

     

Tutorial: Parallel Computing of Simulation Models for Risk Analysis

Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time‐sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation‐based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.