Transition to Non-Offender Self-Narratives: The Emotional Closure Model of Desistance

<p>Although studies have shown that a transition from an ‘offender’ to a ‘non-offender’ self-narrative appears to be associated with desistance, the psychological mechanisms involved in this transition phase have not been explored adequately. This may be because desistance research has primarily been conducted from a criminological perspective, thus social factors (e.g., employment or relationships) have been the focus of enquiry. What little psychologically focused forensic literature there is, is held back by the dominance of the cognitive perspective. Because of this, the role that emotions may play in psychological changes that must take place in order for a person to successfully transition to a non-offender is overlooked. Advances in clinical neuroscience research are increasingly highlighting the significance of emotional processes in psychological functioning. In this thesis I introduce a psychological model of self-narrative by Peter Goldie, who incorporates emotions into his description of the psychological processes that constitute self-narratives. Importantly, Goldie also describes a mechanism of transition from a maladaptive (non-agentic) to an adaptive (agentic) self-narrative. Application of Goldie’s conceptualisation may help to understand how a person who commits offences due to a lack of agency could increase their personal agency and desist. However, as I discuss in chapter one, some persons who commit offences act in a goal-directed manner and thus not due to a lack of personal agency. I will extend Goldie’s conceptualisation of this transition mechanism in order to apply it to the self-narratives of offenders. The adaptation I make to the conceptualisation, which I term, the Emotional Closure Model (ECM), crucially, may explain the transition from offender to non-offender self-narratives for those who both lack agency as well as those who lack motivation to desist. Improved understanding of the psychological mechanisms involved in the transition phase to non-offender self-narratives will have far reaching implications for psychological treatment programmes.</p>

Transition to Non-Offender Self-Narratives: The Emotional Closure Model of Desistance

<p>Although studies have shown that a transition from an ‘offender’ to a ‘non-offender’ self-narrative appears to be associated with desistance, the psychological mechanisms involved in this transition phase have not been explored adequately. This may be because desistance research has primarily been conducted from a criminological perspective, thus social factors (e.g., employment or relationships) have been the focus of enquiry. What little psychologically focused forensic literature there is, is held back by the dominance of the cognitive perspective. Because of this, the role that emotions may play in psychological changes that must take place in order for a person to successfully transition to a non-offender is overlooked. Advances in clinical neuroscience research are increasingly highlighting the significance of emotional processes in psychological functioning. In this thesis I introduce a psychological model of self-narrative by Peter Goldie, who incorporates emotions into his description of the psychological processes that constitute self-narratives. Importantly, Goldie also describes a mechanism of transition from a maladaptive (non-agentic) to an adaptive (agentic) self-narrative. Application of Goldie’s conceptualisation may help to understand how a person who commits offences due to a lack of agency could increase their personal agency and desist. However, as I discuss in chapter one, some persons who commit offences act in a goal-directed manner and thus not due to a lack of personal agency. I will extend Goldie’s conceptualisation of this transition mechanism in order to apply it to the self-narratives of offenders. The adaptation I make to the conceptualisation, which I term, the Emotional Closure Model (ECM), crucially, may explain the transition from offender to non-offender self-narratives for those who both lack agency as well as those who lack motivation to desist. Improved understanding of the psychological mechanisms involved in the transition phase to non-offender self-narratives will have far reaching implications for psychological treatment programmes.</p>

Friendship paradox in growth networks: analytical and empirical analysis

Many empirical studies have shown that in social, citation, collaboration, and other types of networks in real world, the degree of almost every node is less than the average degree of its neighbors. This imbalance is well known in sociology as the friendship paradox and states that your friends are more popular than you on average. If we introduce a value equal to the ratio of the average degree of the neighbors for a certain node to the degree of this node (which is called the ‘friendship index’, FI), then the FI value of more than 1 for most nodes indicates the presence of the friendship paradox in the network. In this paper, we study the behavior of the FI over time for networks generated by growth network models. We will focus our analysis on two models based on the use of the preferential attachment mechanism: the Barabási–Albert model and the triadic closure model. Using the mean-field approach, we obtain differential equations describing the dynamics of changes in the FI over time, and accordingly, after obtaining their solutions, we find the expected values of this index over iterations. The results show that the values of FI are decreasing over time for all nodes in both models. However, for networks constructed in accordance with the triadic closure model, this decrease occurs at a much slower rate than for the Barabási–Albert graphs. In addition, we analyze several real-world networks and show that their FI distributions follow a power law. We show that both the Barabási–Albert and the triadic closure networks exhibit the same behavior. However, for networks based on the triadic closure model, the distributions of FI are more heavy-tailed and, in this sense, are closer to the distributions for real networks.