Following the best of us to help them: Group member prototypicality and collective action

While considering the role of group-level factors as predictors of collective action, research has overlooked the role of group prototypes in determining willingness to engage in collective action. To begin to investigate this area, we conducted two correlational studies (Ns = 141 and 98) in high schools examining the association between prototypical ingroup members’ desire to engage in collective action and participants’ collective action on behalf of a disadvantaged group (immigrants). Results showed a positive association between these two variables. We also investigated boundaries of this effect, finding that the association emerged when participants lacked personal experiences with the disadvantaged group (cross-group friendships; Study 1) or identified more with their ingroup, an effect also found when including a behavioral measure of collective action (Study 2). Intentions to follow the prototypical ingroup member emerged as the mediator (Study 2). It is worth noting that our methodology allowed us to assess prototypicality in a naturalistic context by calculating a metacontrast ratio for each group member, in line with self-categorization theory’s conceptualization of prototypicality. We discuss the theoretical and practical implications, with reference to the role of prototypicality as a means of social influence and to developing social norms in the context of collective action

Health Effects of Late-Career Unemployment

Objective: Job loss has a demonstrated negative impact on physical and mental health. Involuntary retirement has also been linked to poorer physical and mental health outcomes. This study examined whether late-career unemployment is related to involuntary retirement and health declines postretirement. Method: Analysis was conducted using the 2000-2012 U.S. Health and Retirement Study (HRS) survey data with unemployment months regressed with demographic and baseline health measures on physical and mental health. Results: Individuals with late-career unemployment reported more involuntary retirement timing (47.0%) compared with those reporting no unemployment (27.9%). Late-career unemployment had no significant effect on self-reported physical health (β = .003, p = .84), but was significantly associated with lower levels of mental health (β = .039; p \u3c .01). Conclusion: Self-reports of late-career unemployment are not associated with physical health in retirement, but unemployment is associated with involuntary retirement timing and mental health declines in retirement. Unemployment late in the working career should be addressed as a public mental health concern

A Hybrid Approach to Sentiment Analysis with Benchmarking Results

The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Naïve Bayes and Maximum Entropy. Using the well known and publicly available Movie Review Dataset, the proposed hybrid system achieved higher accuracy and precision than Naïve Bayes (NB) and Maximum Entropy (ME)

The Accuracy and Validity of iOS-Based Heart Rate Apps During Moderate to High Intensity Exercise

International Journal of Exercise Science 11(7): 533-540, 2018. People use their smartphones for everything from web browsing to tracking fitness metrics. However, it is unclear whether smartphone-based apps that use photoplethysmography to measure heart rate are an accurate or valid measure of exercise intensity. Purpose was to determine the accuracy and validity of two iOS-based heart rate monitors, Runtastic Heart Rate Monitor and Pulse Tracker PRO by Runtastic (Runtastic) and Instant Heart Rate+: Heart Rate and Pulse Monitor by Azumio (Instant Heart Rate), when compared to the electrocardiogram (ECG) and Polar® T31 uncoded heart rate monitor from moderate to vigorous intensity exercise. Participants were 15 male and female regularly active college students. Pre-exercise heart rate and blood pressure were recorded and then participants exercised on a stationary bike at a pedal rate of between 50-60 rpms. After completing a warm-up stage at 40% of age estimated maximum heart rate (AEMHR), exercise intensity progressed from 50% of AEMHR through to 85% of AEMHR in eight, 5-minute stages. At the end of each stage, and having achieved steady-state, heart rates were recorded from each apparatus. After completing the final stage, participants completed a cooldown at 40% of their AEMHR. Post-exercise heart rate and blood pressure were also recorded to ensure full recovery to baseline. There was a strong positive correlation between the Polar® monitor and the ECG during all stages. However, there were not strong correlations for either of the smartphone-based apps at any time point. Although there were weak correlations between the smartphone-based apps and ECG and Polar®, further studies need to be conducted to determine if inaccuracy is due to user error (finger placement, finger temperature, etc.) or the technology behind the apps

Using self-organizing maps in the visualization and analysis of forest inventory

A lot of useful data on forest condition can be gathered from the Forest Inven­tory (FI). Without the help of data analysis tools, human experts cannot ma­nually interpret information in such a large data set. Conventional multivariate statistical analyses provide results that are difficult to interpret and often do not represent the information in a satisfactory way. Our goal is to identify an alternative approach that will enable fast and efficient interpretation and analysis of the FI data. Such interpretation and analysis can be performed automatically with a clustering method, but all clustering methods have some shortcomings. Therefore, our aim was also to provide information in a form suitable for fast and intuitive visualization. Kohonen’s Self Organizing Map (SOM) is an alternative approach to data visualization and analysis of large multidimensional data sets. SOM provides different possibilities and our experiments are presented with component matrices of individual stand parameters and label matrices. In forming data clusters, we experimented with hierarchi­cal and non hierarchical clustering methods. Our experiments showed that SOM provides useful information in a form suitable for data clustering and data vi­sualization. This enables an efficient analysis of large FI data sets at different analysis scales. Clustering results obtained with SOM and two clustering algorithms are in accordance with ground truth. We have also considered the efficiency of SOM component matrices by visual comparison and correlation among structural parameters and by determining contributions of individual stand parameters to clustering input data. SOM application in visualization and analysis of stand structural parameters enables gathering quickly and efficiently holistic information on the current condition of forest stands and forest ecosystem development. Therefore we recommend the application of Kohonen’s SOM for visualization and analysis of FI data

From prejudice to social change: A social identity perspective

For more than 80 years, understanding the causes, consequences, and remedies for prejudice has been a central theme in social psychology. Prejudice, by definition, refers to the holding of negative attitudes toward others based exclusively on their membership of a given group (Brown, 1995, p. 6). Prejudice is a major area of academic enquiry because it is considered a necessary condition for discrimination, which affects the opportunities and well-being of its targets – the victims. Furthermore, when negative views about a particular group become widespread and shared, then intergroup conflict, violence, and civil unrest are more likely. Much of social psychology, though, has focused on the concepts of prejudice and social change as largely distinct areas of inquiry underpinned by different levels of analysis. Many approaches to explaining prejudice are directed at individual-level factors such as personality and cognitive and motivation processes (which are potentially faulty and irrational). Other explanations of prejudice emphasize the role of system-level factors and argue that maintenance of the status quo and preservation of stable social hierarchies consequently result in the subjugation of particular minority groups. An alternative analysis is that prejudice and social change are both outcomes of ongoing and fluid intergroup relations whereby people's group memberships and relationships between groups play a central explanatory role. The overarching and fundamental questions of interest within this trajectory of work are how is the intergroup relationship perceived now and when and how does it change. Drawing on the social identity perspective, which incorporates both social identity theory (Tajfel & Turner, 1979) and self-categorization theory (Turner, Hogg, Oakes, Reicher, & Wetherell, 1987), the aim of this chapter is to make a case for the interdependence of prejudice and social change. This more integrated analysis relies on a new understanding of prejudice that rejects the premise that such attitudes and associated negative treatment are the product of flawed and faulty cognitive and motivational psychological processes (Oakes, Haslam, & Turner, 1994; Reynolds, Haslam, & Turner, 2012). Instead prejudice needs to be conceptualized, first and foremost, as an outcome of group processes and intergroup dynamics, whereby members of the majority and minority groups are positioned in a particular social relationship. Majority and minority do not refer to the simple numbers but to positions of power through cultural and economic dominance within a social system