The Complexity of Social Outcomes from Cooperative,Competitive, and Individualistic Reward Systems

Previous research on cooperative and competitive reward systems has investigated the relation between extreme cooperative and competitive conditions, along with an intermediate noninterdependent neutral condition, to numerous outcome variables. This study added two additional conditions to these three usual conditions, between the neutral midpoint and the cooperative or competitive extremes, to see if these intermediate conditions might be distinctive in the outcomes they produced. The study used 240 participants, divided into groups of three that played a board game under these five different reward conditions. Participants' attitudes toward self, others, and task were then assessed and analyzed along with objective measures of performance, measures of self-esteem, state and trait anxiety, and results coded from an autobiographical report in game-defined roles. Results indicated that the intermediate cooperative condition was distinctively different from the extreme cooperative condition in predicted ways, and that the intermediate competitive condition was distinctively different from the extreme competitive condition, but in unpredicted ways. The research also demonstrated that an individualistic condition, which had previously been thought to produce neither a cooperative nor competitive social orientation, in fact produced both, raising questions as to whether reward interdependence, as researchers have defined it, is really the cause of cooperation and competition.     

The inflationary aspect of repetitive devaluation: A comment

In this paper we use primary data on 500 Ghanaian FBOs collected through semi-structured interviews and risky dictator games (RDG) to test the validity of the cooperative life cycle theory and formulate a measure of cooperative health. We first define cooperative health as the alignment of heterogeneity in risk preferences and the effectuation of collective investments. We then use cluster and correlation analysis to categorize FBOs on the basis of their health and correlate these typologies with various performance indicators. Our findings reveal that organizational health is generally low as there are only a few organizations that manage to provide member-farmers with both risk-sharing and cost-saving opportunities. Further, healthier FBOs experience stronger growth in membership while health is lower in FBOs that have been established for the purpose of benefitting from external incentives.     

The Enemy Within: The Outlaw Motorcycle Gang Moral Panic

When there is a period devoid of sensationalistic biker news, the public’s concern about outlaw bikers can be quite low. All it takes is one overt act of violence to heighten the public’s fear of Outlaw Motorcycle Gangs. Outlaw Motorcycle Gangs are the most current “dangerous class” which have been the subject of a moral panic in Canada. Outlaw Motorcycle Gangs as organized crime bodies are deemed to be the single most serious threat to the country—the enemy within. The gang war in British Columbia is fuelled by drugs supplied by the Hells Angels and there is a wide spread fear that these evildoers are trying to harm and tear apart the fabric of our society. This article provides a critical in-depth analysis of the construction of an Outlaw Motorcycle Gang moral panic with the focus on Canada. The roles played by the media, politicians, and the public in using isolated, violent incidents to support their interests will be examined along with the interactions between these groups and the resultant punitive actions directed toward members of Outlaw Motorcycle Gangs.     

Classification Improvements in Automated Gunshot Residue (GSR) Scans

Classification of particles as gunshot residues (GSRs) is conducted using a semiautomatic approach in which the system first classifies particles based on an automatic elemental analysis, and then, examiners manually analyze particles having compositions which are characteristic of or consistent with GSRs. Analyzing all the particles in the second stage is time consuming with many particles classified by the initial automated system as being potentially GSRs excluded as such by the forensic examiner. In this paper, a new algorithm is developed to improve the initial classification step. The algorithm is based on a binary tree that was trained on almost 16,000 particles from 43 stubs used to sample hands of suspects. The classification algorithm was tested on 5,900 particles from 23 independent stubs and performed very well in terms of false positive and false negative rates. A routine use of the new algorithm can reduce significantly the analysis time of GSRs.