Breadth Of Situations Research Articles

Development and validation of the Ryerson Social Anxiety Scales (RSAS)

ABSTRACT Background: Extant self-report measures of social anxiety primarily assess the breadth of social situations in which respondents feel anxious, rather than assessing severity in terms of the distress and impairment that individuals experience due to their social anxiety symptoms. This paper describes the development and validation of the Ryerson Social Anxiety Scales (RSAS; Rogojanski et al., 2019; see Appendix), a new measure for assessing both the breadth of situations that trigger social anxiety and the severity (i.e., distress and impairment) associated with social anxiety, across two studies. Method/Design: Two samples of university students (N = 501 total) completed demographic and self-report symptom measures. In Study 1, participants completed the RSAS and several other measures of psychological symptoms. In Study 2, participants completed the same measures and were also assessed for the presence of Social Anxiety Disorder (SAD) using a semistructured clinical interview. Results: Across both samples, the RSAS demonstrated excellent internal consistency and incremental validity. It consistently emerged as a unique predictor of psychosocial impairment. In Study 2, increases in RSAS scores were associated with increased odds of having SAD. Conclusions: The RSAS has robust psychometric properties and fills an important gap among available measures for assessing SAD severity.

Sustainability, robustness, and resilience metrics for water and other infrastructure systems

Metrics representing water resources security (Sustainability, Robustness, Resilience – SRR) were adopted from reliability engineering to quantify stress on water systems in both the demand and supply sectors. The metrics were calculated for Prescott Valley, AZ and a hypothetical water system to illustrate a method of application, the breadth of situations in which they are useful and their response to water management and system design decisions. The sensitivities of the proposed metrics to (i) variation in supply and demand functions, (ii) surface reservoir storage capacity, and (iii) reclaimed water use were investigated. Strengths and weaknesses of the proposed metrics are discussed. The water security metrics can contribute to the multi-objective evaluation of water resources planning alternatives. The fundamental nature of these metrics taken from broadly applicable field of reliability engineering suggests that they can be generally applied to single and joint infrastructure SRR assessment, planning, and design.

Landscape as a Driver for Well-being: The ELC in the Globalist Arena

The European Landscape Convention (ELC) recognizes that landscapes are dynamic entities that change over time and advocates appropriate management strategies that will protect landscape values and the well-being of communities and individuals affected by change. Global drivers are however often powerful forces to which the inhabitants of landscape don't have means to resist. Two cases of dramatic landscape change, in the contrasting geographies and political contexts of New Zealand and Palestine, are presented. The first example is located within a benign context and the other in an area of extreme conflict. Nonetheless in both cases the changes described have striking visible impact on the landscape and significant flow-on effects, some of them intangible and unquantifiable, on the well-being of the people who inhabit these landscapes. These cases present the two ends of a spectrum in which the hypothesis of a world landscape convention inspired by the ELC is relevant. The argument is that the moral imperative of a landscape convention in the spirit of the ELC holds the potential to become the mechanism to mitigate such ill effects of landscape change at a breadth of situations from the everyday ordinary landscape to military conflict zones.

Ability, Breadth, and Parsimony in Computational Models of Higher‐Order Cognition

Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern-day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher-order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.